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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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Justifying the Justification Hypothesis: scientific-humanism, Equilintegration (EI) Theory, and the Beliefs, Events, and Values Inventory (BEVI)

Affiliation.

• 1 James Madison University, Harrisonburg, VA 22807, USA. [email protected]
• PMID: 15558624
• DOI: 10.1002/jclp.20092

The Justification Hypothesis (JH; Henriques, 2003) is a basic, general, and macro-level construct that is highly compelling. However, it needs greater specification (i.e., justification) regarding what it is, how it might be operationalized and measured, and what it does and does not predict in the real world. In the present analysis, the act of "justification" is conceptualized as the ongoing attempt to convince self and/or others that one's beliefs and values, which is to say one's "version of reality" or VOR, is correct, defensible, and good. In addressing these issues, this paper is divided into two complementary parts: (a) consideration of justification dynamics and exemplars from a scientific-humanist perspective and (b) an examination of how justification systems and processes have been studied vis-a-vis research and theory on beliefs and values as well as an extant model--Equilintegration (EI) Theory--and method--the Beliefs, Events, and Values Inventory (BEVI).

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Understanding our justification systems, five key concepts that stem from the justification hypothesis.

Posted November 1, 2013

“I am too stupid for that,” the woman (I’ll call her Annie) said to me after I asked her what prevented her from going on to pursue a college degree. I asked this question in the context of a psychiatric evaluation I was doing. Annie had been hospitalized following an overdose that she genuinely thought would kill her. A standardized assessment of her intellect had documented that her IQ was in the High Average Range, and in no way shape or form was she “stupid.” But that is how she saw herself. In fact, she saw herself as weak or lacking in most every respect. Although she was of average attractiveness , she saw herself as ugly. Although she could play musical instruments, she saw herself as having no talents. Indeed, she was almost relentless in seeing herself as worthless and inferior. Diagnostically, she met criteria for an Avoidant Personality Disorder and a “double depression .”

I was doing this evaluation at a time when I was thinking a lot about human self-consciousness. As part of my dissertation, I was studying cognitive biases and the fact that most people self-enhance and seek to paint themselves in the most rosy picture the data can justify. But although true of most, people with depression tended not to do this, or at least do it less, with some being grossly self-critical. I was also getting my first real deep exposure to modern psychodynamic theory, and learning about how the self-consciousness system often filters out subconscious motivation . In the language of dynamic defense mechanisms , the woman was “turning against the self.” which is often thought to be a way of redirecting aggression that is dangerous to express. Ultimately, my study of evolutionary theory, social and cognitive psychology, and psychodynamic theory intersected with hearing Annie’s story, and a light bulb went off that allowed me to realize that self-enhancement and turning against the self might be both part of the same basic processes. The light bulb would reveal a key design feature of human self-consciousness.

One of the most obvious features of Annie’s story was her family history. In what is an all-to-common story line to those who work in psychiatric settings, this woman’s father was a domineering and abusive man, who ruled the household with an iron fist. Her mother was a timid woman, who obviously walked on eggshells. Interestingly, although her father was verbally abusive toward Annie, he was never physically abusive. But he was physically abusive to her older brother. Indeed, she had several distinctive memories of her father beating her brother, telling her brother that he ought to be more like his obedient sister. Upon hearing that aspect of her story, something clicked and, over time, a whole new understanding of self-consciousness emerged in me.

At a primal level, this woman watched her brother be beaten, and yet it was a fate she always somehow avoided. How did she avoid it? One very plausible answer is by turning against herself. As her father behaved the way he did, a natural response would to become angry and defiant and challenging. And, yet, doing so would be highly dangerous. If, on the other hand, she saw herself as weak, unworthy, and undeserving, these beliefs justify submission and deference, which influenced her father’s behavior, namely by avoiding an attack.

In this light then, Annie’s sense that she was lesser than others perhaps stemmed from the same basic process that “normals” engage in when they moderately self-enhance. By thinking that we are a bit smarter, more attractive, or more effective than others, surely that places us in a position to frame what we do in a more influential way. In short, although very different in content, both self-criticalness and self-enhancement might tie into the process of justifying one’s actions to one’s self in a social context. Ultimately, the exchange with Annie led me to formulate the “ Justification Hypothesis ” which is the idea that our self-consciousness system emerged in the evolutionary landscape as a social reason giving system. That is, we humans were not shaped by evolution for general abstract reasoning, but our reasoning capacities emerged in the context of a social environment where the adaptive task was to generate social reasons for our actions.

Although there are many different directions the Justification Hypothesis can take us, there are five general key concepts that fall from this analysis that I believe people everywhere can benefit from understanding.

1. Language-based beliefs and values are organized into systems of justification . Beliefs and values are not randomly distributed in people’s heads, but instead are networked together to form systems that ultimately function to legitimize action.

2. Justification systems exist at the individual and societal level, and the two are connected . I have my justifications for why I am the way I am. I can explain to my kids why they should follow what I say or to my colleagues why my ideas are valid. At the same time, large scale justifications provide the glue that gives a society a shared identity and worldview in a particular context. I reason the way I do in a way that is deeply connected to my sociocultural context. Think, for example, of how justifications regarding gender roles are different in Canada as opposed to Saudi Arabi, or how they compare today relative to 100 years ago. We exist in a sea of justification and carve out our individual path in that context.

3. Humans have two streams of consciousness, experiencing and justifying. The justifying capacity of humans is a late evolutionary add on. Prior to it, others animals have been navigating the environment for eons via sensory-perceptual-emotional processes of action investment. Thus, these are two very different systems, the mutual flow of which makes up human consciousness in its totality.

4. For adults, there are two domains of justification, the private and the public. As children, we download the reason giving systems of the social context in which we are born. As our capacities develop, we begin to engage in reasoning on our own. Privately, we must legitimize our own thoughts and develop narrative for who we are and why we do what we do. And, our family, friends, peers, etc. will often want to be informed about what we think and why we do what we do.

5. There is filtering between the experiential, private and public justification systems. In the film Liar Liar , Jim Carey plays a cheese ball lawyer who always is “BSing” his friends, adversaries and family.

The premise of the movie is that his son gets so frustrated by this he wishes his father could not tell a lie for 24 hours, and the movie is largely about the comical situational analysis of the character as the boy’s wish miraculously comes true and the lawyer must say exactly what his private narrator thinks. As such, the movie is a nice example of the private to public filter. Indeed, we all have experience with this, which can be noticed with the question, “What if everyone of your thoughts became public to everyone?” I don’t know anyone who doesn’t find that to be a somewhat troubling thought. The private to public filter is called the Rogerian filter because we generally filter to avoid injuring others in a way that will evoke problematic reactions and judgments.

But, as Freud so clearly noted, we also filter out feelings, images, wishes (experiential processes) from our self-conscious system.

In his book Ego Defenses and the Legitimization of Behavior, Swanson (1988) made exactly this point, explicitly arguing that we should think of all ego defenses as “justifications that people make to themselves and others—justifications so designed that the defender, not just other people, can accept them.” Think back to Annie. It seems highly likely that she had hostile feelings toward her father. However, if those created anxiety , then the belief that she was deserving in some way of his abuse would block her from those feelings. They would be filtered out, or repressed and her system would be returned to equilibrium by the rationalization that she was worthless.

The following diagram depicts the three domains of consciousness and the filters.

I believe that justification systems are one of the most central aspects of human psychology to be understood. I hope this blog jives with your justification system and you find these insights foster a greater understanding of the nature of your justification system and how it relates to your experiential mind and the socio-cultural context in which you live.

Gregg Henriques, Ph.D. , is a professor of psychology at James Madison University.

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Scientific Hypotheses: Writing, Promoting, and Predicting Implications

Armen yuri gasparyan.

1 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, UK.

Lilit Ayvazyan

2 Department of Medical Chemistry, Yerevan State Medical University, Yerevan, Armenia.

Ulzhan Mukanova

3 Department of Surgical Disciplines, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

George D. Kitas

5 Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester, UK.

Scientific hypotheses are essential for progress in rapidly developing academic disciplines. Proposing new ideas and hypotheses require thorough analyses of evidence-based data and predictions of the implications. One of the main concerns relates to the ethical implications of the generated hypotheses. The authors may need to outline potential benefits and limitations of their suggestions and target widely visible publication outlets to ignite discussion by experts and start testing the hypotheses. Not many publication outlets are currently welcoming hypotheses and unconventional ideas that may open gates to criticism and conservative remarks. A few scholarly journals guide the authors on how to structure hypotheses. Reflecting on general and specific issues around the subject matter is often recommended for drafting a well-structured hypothesis article. An analysis of influential hypotheses, presented in this article, particularly Strachan's hygiene hypothesis with global implications in the field of immunology and allergy, points to the need for properly interpreting and testing new suggestions. Envisaging the ethical implications of the hypotheses should be considered both by authors and journal editors during the writing and publishing process.

INTRODUCTION

We live in times of digitization that radically changes scientific research, reporting, and publishing strategies. Researchers all over the world are overwhelmed with processing large volumes of information and searching through numerous online platforms, all of which make the whole process of scholarly analysis and synthesis complex and sophisticated.

Current research activities are diversifying to combine scientific observations with analysis of facts recorded by scholars from various professional backgrounds. 1 Citation analyses and networking on social media are also becoming essential for shaping research and publishing strategies globally. 2 Learning specifics of increasingly interdisciplinary research studies and acquiring information facilitation skills aid researchers in formulating innovative ideas and predicting developments in interrelated scientific fields.

Arguably, researchers are currently offered more opportunities than in the past for generating new ideas by performing their routine laboratory activities, observing individual cases and unusual developments, and critically analyzing published scientific facts. What they need at the start of their research is to formulate a scientific hypothesis that revisits conventional theories, real-world processes, and related evidence to propose new studies and test ideas in an ethical way. 3 Such a hypothesis can be of most benefit if published in an ethical journal with wide visibility and exposure to relevant online databases and promotion platforms.

Although hypotheses are crucially important for the scientific progress, only few highly skilled researchers formulate and eventually publish their innovative ideas per se . Understandably, in an increasingly competitive research environment, most authors would prefer to prioritize their ideas by discussing and conducting tests in their own laboratories or clinical departments, and publishing research reports afterwards. However, there are instances when simple observations and research studies in a single center are not capable of explaining and testing new groundbreaking ideas. Formulating hypothesis articles first and calling for multicenter and interdisciplinary research can be a solution in such instances, potentially launching influential scientific directions, if not academic disciplines.

The aim of this article is to overview the importance and implications of infrequently published scientific hypotheses that may open new avenues of thinking and research.

Despite the seemingly established views on innovative ideas and hypotheses as essential research tools, no structured definition exists to tag the term and systematically track related articles. In 1973, the Medical Subject Heading (MeSH) of the U.S. National Library of Medicine introduced “Research Design” as a structured keyword that referred to the importance of collecting data and properly testing hypotheses, and indirectly linked the term to ethics, methods and standards, among many other subheadings.

One of the experts in the field defines “hypothesis” as a well-argued analysis of available evidence to provide a realistic (scientific) explanation of existing facts, fill gaps in public understanding of sophisticated processes, and propose a new theory or a test. 4 A hypothesis can be proven wrong partially or entirely. However, even such an erroneous hypothesis may influence progress in science by initiating professional debates that help generate more realistic ideas. The main ethical requirement for hypothesis authors is to be honest about the limitations of their suggestions. 5

EXAMPLES OF INFLUENTIAL SCIENTIFIC HYPOTHESES

Daily routine in a research laboratory may lead to groundbreaking discoveries provided the daily accounts are comprehensively analyzed and reproduced by peers. The discovery of penicillin by Sir Alexander Fleming (1928) can be viewed as a prime example of such discoveries that introduced therapies to treat staphylococcal and streptococcal infections and modulate blood coagulation. 6 , 7 Penicillin got worldwide recognition due to the inventor's seminal works published by highly prestigious and widely visible British journals, effective ‘real-world’ antibiotic therapy of pneumonia and wounds during World War II, and euphoric media coverage. 8 In 1945, Fleming, Florey and Chain got a much deserved Nobel Prize in Physiology or Medicine for the discovery that led to the mass production of the wonder drug in the U.S. and ‘real-world practice’ that tested the use of penicillin. What remained globally unnoticed is that Zinaida Yermolyeva, the outstanding Soviet microbiologist, created the Soviet penicillin, which turned out to be more effective than the Anglo-American penicillin and entered mass production in 1943; that year marked the turning of the tide of the Great Patriotic War. 9 One of the reasons of the widely unnoticed discovery of Zinaida Yermolyeva is that her works were published exclusively by local Russian (Soviet) journals.

The past decades have been marked by an unprecedented growth of multicenter and global research studies involving hundreds and thousands of human subjects. This trend is shaped by an increasing number of reports on clinical trials and large cohort studies that create a strong evidence base for practice recommendations. Mega-studies may help generate and test large-scale hypotheses aiming to solve health issues globally. Properly designed epidemiological studies, for example, may introduce clarity to the hygiene hypothesis that was originally proposed by David Strachan in 1989. 10 David Strachan studied the epidemiology of hay fever in a cohort of 17,414 British children and concluded that declining family size and improved personal hygiene had reduced the chances of cross infections in families, resulting in epidemics of atopic disease in post-industrial Britain. Over the past four decades, several related hypotheses have been proposed to expand the potential role of symbiotic microorganisms and parasites in the development of human physiological immune responses early in life and protection from allergic and autoimmune diseases later on. 11 , 12 Given the popularity and the scientific importance of the hygiene hypothesis, it was introduced as a MeSH term in 2012. 13

Hypotheses can be proposed based on an analysis of recorded historic events that resulted in mass migrations and spreading of certain genetic diseases. As a prime example, familial Mediterranean fever (FMF), the prototype periodic fever syndrome, is believed to spread from Mesopotamia to the Mediterranean region and all over Europe due to migrations and religious prosecutions millennia ago. 14 Genetic mutations spearing mild clinical forms of FMF are hypothesized to emerge and persist in the Mediterranean region as protective factors against more serious infectious diseases, particularly tuberculosis, historically common in that part of the world. 15 The speculations over the advantages of carrying the MEditerranean FeVer (MEFV) gene are further strengthened by recorded low mortality rates from tuberculosis among FMF patients of different nationalities living in Tunisia in the first half of the 20th century. 16

Diagnostic hypotheses shedding light on peculiarities of diseases throughout the history of mankind can be formulated using artefacts, particularly historic paintings. 17 Such paintings may reveal joint deformities and disfigurements due to rheumatic diseases in individual subjects. A series of paintings with similar signs of pathological conditions interpreted in a historic context may uncover mysteries of epidemics of certain diseases, which is the case with Ruben's paintings depicting signs of rheumatic hands and making some doctors to believe that rheumatoid arthritis was common in Europe in the 16th and 17th century. 18

WRITING SCIENTIFIC HYPOTHESES

There are author instructions of a few journals that specifically guide how to structure, format, and make submissions categorized as hypotheses attractive. One of the examples is presented by Med Hypotheses , the flagship journal in its field with more than four decades of publishing and influencing hypothesis authors globally. However, such guidance is not based on widely discussed, implemented, and approved reporting standards, which are becoming mandatory for all scholarly journals.

Generating new ideas and scientific hypotheses is a sophisticated task since not all researchers and authors are skilled to plan, conduct, and interpret various research studies. Some experience with formulating focused research questions and strong working hypotheses of original research studies is definitely helpful for advancing critical appraisal skills. However, aspiring authors of scientific hypotheses may need something different, which is more related to discerning scientific facts, pooling homogenous data from primary research works, and synthesizing new information in a systematic way by analyzing similar sets of articles. To some extent, this activity is reminiscent of writing narrative and systematic reviews. As in the case of reviews, scientific hypotheses need to be formulated on the basis of comprehensive search strategies to retrieve all available studies on the topics of interest and then synthesize new information selectively referring to the most relevant items. One of the main differences between scientific hypothesis and review articles relates to the volume of supportive literature sources ( Table 1 ). In fact, hypothesis is usually formulated by referring to a few scientific facts or compelling evidence derived from a handful of literature sources. 19 By contrast, reviews require analyses of a large number of published documents retrieved from several well-organized and evidence-based databases in accordance with predefined search strategies. 20 , 21 , 22

The format of hypotheses, especially the implications part, may vary widely across disciplines. Clinicians may limit their suggestions to the clinical manifestations of diseases, outcomes, and management strategies. Basic and laboratory scientists analysing genetic, molecular, and biochemical mechanisms may need to view beyond the frames of their narrow fields and predict social and population-based implications of the proposed ideas. 23

Advanced writing skills are essential for presenting an interesting theoretical article which appeals to the global readership. Merely listing opposing facts and ideas, without proper interpretation and analysis, may distract the experienced readers. The essence of a great hypothesis is a story behind the scientific facts and evidence-based data.

ETHICAL IMPLICATIONS

The authors of hypotheses substantiate their arguments by referring to and discerning rational points from published articles that might be overlooked by others. Their arguments may contradict the established theories and practices, and pose global ethical issues, particularly when more or less efficient medical technologies and public health interventions are devalued. The ethical issues may arise primarily because of the careless references to articles with low priorities, inadequate and apparently unethical methodologies, and concealed reporting of negative results. 24 , 25

Misinterpretation and misunderstanding of the published ideas and scientific hypotheses may complicate the issue further. For example, Alexander Fleming, whose innovative ideas of penicillin use to kill susceptible bacteria saved millions of lives, warned of the consequences of uncontrolled prescription of the drug. The issue of antibiotic resistance had emerged within the first ten years of penicillin use on a global scale due to the overprescription that affected the efficacy of antibiotic therapies, with undesirable consequences for millions. 26

The misunderstanding of the hygiene hypothesis that primarily aimed to shed light on the role of the microbiome in allergic and autoimmune diseases resulted in decline of public confidence in hygiene with dire societal implications, forcing some experts to abandon the original idea. 27 , 28 Although that hypothesis is unrelated to the issue of vaccinations, the public misunderstanding has resulted in decline of vaccinations at a time of upsurge of old and new infections.

A number of ethical issues are posed by the denial of the viral (human immunodeficiency viruses; HIV) hypothesis of acquired Immune deficiency Syndrome (AIDS) by Peter Duesberg, who overviewed the links between illicit recreational drugs and antiretroviral therapies with AIDS and refuted the etiological role of HIV. 29 That controversial hypothesis was rejected by several journals, but was eventually published without external peer review at Med Hypotheses in 2010. The publication itself raised concerns of the unconventional editorial policy of the journal, causing major perturbations and more scrutinized publishing policies by journals processing hypotheses.

WHERE TO PUBLISH HYPOTHESES

Although scientific authors are currently well informed and equipped with search tools to draft evidence-based hypotheses, there are still limited quality publication outlets calling for related articles. The journal editors may be hesitant to publish articles that do not adhere to any research reporting guidelines and open gates for harsh criticism of unconventional and untested ideas. Occasionally, the editors opting for open-access publishing and upgrading their ethics regulations launch a section to selectively publish scientific hypotheses attractive to the experienced readers. 30 However, the absence of approved standards for this article type, particularly no mandate for outlining potential ethical implications, may lead to publication of potentially harmful ideas in an attractive format.

A suggestion of simultaneously publishing multiple or alternative hypotheses to balance the reader views and feedback is a potential solution for the mainstream scholarly journals. 31 However, that option alone is hardly applicable to emerging journals with unconventional quality checks and peer review, accumulating papers with multiple rejections by established journals.

A large group of experts view hypotheses with improbable and controversial ideas publishable after formal editorial (in-house) checks to preserve the authors' genuine ideas and avoid conservative amendments imposed by external peer reviewers. 32 That approach may be acceptable for established publishers with large teams of experienced editors. However, the same approach can lead to dire consequences if employed by nonselective start-up, open-access journals processing all types of articles and primarily accepting those with charged publication fees. 33 In fact, pseudoscientific ideas arguing Newton's and Einstein's seminal works or those denying climate change that are hardly testable have already found their niche in substandard electronic journals with soft or nonexistent peer review. 34

CITATIONS AND SOCIAL MEDIA ATTENTION

The available preliminary evidence points to the attractiveness of hypothesis articles for readers, particularly those from research-intensive countries who actively download related documents. 35 However, citations of such articles are disproportionately low. Only a small proportion of top-downloaded hypotheses (13%) in the highly prestigious Med Hypotheses receive on average 5 citations per article within a two-year window. 36

With the exception of a few historic papers, the vast majority of hypotheses attract relatively small number of citations in a long term. 36 Plausible explanations are that these articles often contain a single or only a few citable points and that suggested research studies to test hypotheses are rarely conducted and reported, limiting chances of citing and crediting authors of genuine research ideas.

A snapshot analysis of citation activity of hypothesis articles may reveal interest of the global scientific community towards their implications across various disciplines and countries. As a prime example, Strachan's hygiene hypothesis, published in 1989, 10 is still attracting numerous citations on Scopus, the largest bibliographic database. As of August 28, 2019, the number of the linked citations in the database is 3,201. Of the citing articles, 160 are cited at least 160 times ( h -index of this research topic = 160). The first three citations are recorded in 1992 and followed by a rapid annual increase in citation activity and a peak of 212 in 2015 ( Fig. 1 ). The top 5 sources of the citations are Clin Exp Allergy (n = 136), J Allergy Clin Immunol (n = 119), Allergy (n = 81), Pediatr Allergy Immunol (n = 69), and PLOS One (n = 44). The top 5 citing authors are leading experts in pediatrics and allergology Erika von Mutius (Munich, Germany, number of publications with the index citation = 30), Erika Isolauri (Turku, Finland, n = 27), Patrick G Holt (Subiaco, Australia, n = 25), David P. Strachan (London, UK, n = 23), and Bengt Björksten (Stockholm, Sweden, n = 22). The U.S. is the leading country in terms of citation activity with 809 related documents, followed by the UK (n = 494), Germany (n = 314), Australia (n = 211), and the Netherlands (n = 177). The largest proportion of citing documents are articles (n = 1,726, 54%), followed by reviews (n = 950, 29.7%), and book chapters (n = 213, 6.7%). The main subject areas of the citing items are medicine (n = 2,581, 51.7%), immunology and microbiology (n = 1,179, 23.6%), and biochemistry, genetics and molecular biology (n = 415, 8.3%).

Interestingly, a recent analysis of 111 publications related to Strachan's hygiene hypothesis, stating that the lack of exposure to infections in early life increases the risk of rhinitis, revealed a selection bias of 5,551 citations on Web of Science. 37 The articles supportive of the hypothesis were cited more than nonsupportive ones (odds ratio adjusted for study design, 2.2; 95% confidence interval, 1.6–3.1). A similar conclusion pointing to a citation bias distorting bibliometrics of hypotheses was reached by an earlier analysis of a citation network linked to the idea that β-amyloid, which is involved in the pathogenesis of Alzheimer disease, is produced by skeletal muscle of patients with inclusion body myositis. 38 The results of both studies are in line with the notion that ‘positive’ citations are more frequent in the field of biomedicine than ‘negative’ ones, and that citations to articles with proven hypotheses are too common. 39

Social media channels are playing an increasingly active role in the generation and evaluation of scientific hypotheses. In fact, publicly discussing research questions on platforms of news outlets, such as Reddit, may shape hypotheses on health-related issues of global importance, such as obesity. 40 Analyzing Twitter comments, researchers may reveal both potentially valuable ideas and unfounded claims that surround groundbreaking research ideas. 41 Social media activities, however, are unevenly distributed across different research topics, journals and countries, and these are not always objective professional reflections of the breakthroughs in science. 2 , 42

Scientific hypotheses are essential for progress in science and advances in healthcare. Innovative ideas should be based on a critical overview of related scientific facts and evidence-based data, often overlooked by others. To generate realistic hypothetical theories, the authors should comprehensively analyze the literature and suggest relevant and ethically sound design for future studies. They should also consider their hypotheses in the context of research and publication ethics norms acceptable for their target journals. The journal editors aiming to diversify their portfolio by maintaining and introducing hypotheses section are in a position to upgrade guidelines for related articles by pointing to general and specific analyses of the subject, preferred study designs to test hypotheses, and ethical implications. The latter is closely related to specifics of hypotheses. For example, editorial recommendations to outline benefits and risks of a new laboratory test or therapy may result in a more balanced article and minimize associated risks afterwards.

Not all scientific hypotheses have immediate positive effects. Some, if not most, are never tested in properly designed research studies and never cited in credible and indexed publication outlets. Hypotheses in specialized scientific fields, particularly those hardly understandable for nonexperts, lose their attractiveness for increasingly interdisciplinary audience. The authors' honest analysis of the benefits and limitations of their hypotheses and concerted efforts of all stakeholders in science communication to initiate public discussion on widely visible platforms and social media may reveal rational points and caveats of the new ideas.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Gasparyan AY, Yessirkepov M, Kitas GD.
• Methodology: Gasparyan AY, Mukanova U, Ayvazyan L.
• Writing - original draft: Gasparyan AY, Ayvazyan L, Yessirkepov M.
• Writing - review & editing: Gasparyan AY, Yessirkepov M, Mukanova U, Kitas GD.

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Philosophical skepticism is interesting because there are intriguing arguments for it despite its initial implausibility. Many contemporary epistemological positions can be fruitfully presented as responding to some aspect of those arguments. For example, questions regarding principles of epistemic closure and transmission are closely related to the discussion of what we will call Cartesian Skepticism, as are views according to which we are entitled to dismiss skeptical hypotheses even though we do not have evidence against them. The traditional issue of the structure of knowledge and justification, engendering Foundationalism, Coherentism, and Infinitism, can be seen as resulting from one main argument for what we will call Pyrrhonian Skepticism. In what follows we present these two forms of skepticism and assess the main arguments for them.

1. Knowledge, Justification and Skepticism

2. two basic forms of philosophical skepticism, 3.1 consideration of cp1, 3.2 consideration of cp2, 4. contextualism, 5.1 rejecting premise 2: foundationalism, 5.2 rejecting premise 5: infinitism, 5.3 rejecting premise 3: coherentism, 5.4 rejecting premise 7: positism, 5.5 rejecting more than one premise, other internet resources, related entries.

Philosophically interesting forms of skepticism claim that we do not know propositions which we ordinarily think we do know. We should distinguish such skepticism from the ordinary kind, the claim that we do not know propositions which we would gladly grant not to know. Thus, it is a form of ordinary skepticism to say that we do not know that there are an even number of stars in the Milky Way, but it is a form of philosophical skepticism to say that we do not know that the sun will come out tomorrow. Even though our interest is in philosophical skepticism, we can start our inquiry by thinking about ordinary skepticism.

Why do we readily grant, then, that we don’t know that there are an even number of stars in the Milky Way? To begin with, the vast majority of us do not even believe that proposition, and it is widely acknowledged that knowledge requires belief. [ 1 ] But even those who believe it do not know it, even if they luck out and it is true. They do not know it because they are not justified in believing it, and knowledge requires justification. [ 2 ] Of course, they are not justified in disbelieving that proposition either. Belief and disbelief are two of the so-called doxastic attitudes that we can adopt towards a proposition. We can also, of course, not even consider a proposition, and thus not adopt any doxastic attitude towards it. But most philosophers would hold that in addition to belief and disbelief there is a third possible doxastic attitude that we can adopt towards a proposition: we can suspend judgment (or withhold assent) with respect to it. Suspension of judgment is thus a bona fide doxastic attitude alongside belief and disbelief, and is not to be equated with the failure to adopt any doxastic attitude. [ 3 ] Because it is a genuine doxastic attitude, suspension of judgment (just like belief and disbelief, and unlike the failure to form any doxastic attitude) can itself be justified or unjustified. For instance, we would ordinarily think that suspension of judgment is not justified with respect to the proposition that Paris is the Capital of France, but it is with respect to the proposition that there are an even number of stars in the Milky Way.

Some arguments for philosophical skepticism target knowledge directly, not concerning themselves with justification. For instance, some argue that we do not know certain propositions because our beliefs in them are not sensitive (in a sense to be explained below), and they claim that sensitivity is a condition on knowledge—but perhaps not on justified belief. We will examine the bearing of the sensitivity condition on skeptical arguments assuming that it applies to justification. But even if an argument for philosophical skepticism targets our knowledge in a certain area while remaining silent about whether we have justified beliefs in that area, that argument will still indirectly target our justification as well. For, if the argument succeeds, then it provides us with knowledge (or at least justified belief) that we do not know a certain proposition p . And it is plausible to hold that if we know (or justifiably believe) that we do not know a proposition p , then we are not even justified in believing p .

In what follows, then, we identify skepticism with respect to a field of propositions F as the claim that the only justified attitude with respect to propositions in F is suspension of judgment. Philosophical skepticism, then, differs from ordinary skepticism at least regarding the field of propositions to which it is claimed to apply. But even within the realm of philosophical skepticism we can make an interesting distinction by appealing to the scope of the thesis.

One interesting distinction between kinds of philosophical skepticism pertains to the question whether they iterate. Thus, consider skepticism about the future: the claim that the only justified attitude with respect to propositions about the future is suspension of judgment. That kind of philosophical skepticism overlaps partly with ordinary skepticism about the future. We should all grant, for instance, that we should suspend judgment with respect to the proposition that the flip of this fair coin in the next second will come up heads, but most of us think that we should believe, not suspend judgment with respect to, the proposition that the sun will come out tomorrow. Being a skeptic with respect to the first-order proposition that the sun will come out tomorrow (that is to say, holding that the only justified attitude with respect to that proposition is suspension of judgment) can be combined with any of the three doxastic attitudes with respect to the second-order proposition that the only justified attitude with respect to the proposition that the sun will come out tomorrow is to suspend judgment. Generalizing, whenever the skeptic holds that the only justified attitude with respect to a field of propositions F is to suspend judgment, we can ask them which attitude is justified with respect to the proposition that the only justified attitude with respect to any proposition in F is suspension of judgment. Perhaps the most straightforward answer here is that the only justified attitude with respect to that second-order proposition is belief. After all, isn’t skepticism with respect to F precisely the belief that we should suspend judgment with respect to any proposition in F ? We will call this combination of views—the view that we should suspend judgment with respect to any proposition in F and believe the proposition that we should suspend judgment with respect to any proposition in F —“Cartesian Skepticism”, because of the skeptical arguments investigated by Descartes and his critics in the mid-seventeenth century. Other philosophers, following an ancient tradition, refer to this view as “Academic Skepticism” (see the entry on ancient skepticism ).

But some skeptics are skeptics regarding second- (and higher-) order propositions as well as regarding first-order propositions. Following the same ancient tradition, we will call that kind of skepticism “Pyrrhonian Skepticism”. Without any claim to historical accuracy, we will take Pyrrhonian Skepticism to be absolute skepticism—the thesis that suspension of judgment is the only justified attitude with respect to any proposition p . Is Pyrrhonian Skepticism so understood self-refuting? It is certainly formally consistent: no contradiction follows just from the propositions that the only justified attitude with respect to the proposition that p is suspension of judgment and that the only justified attitude with respect to the proposition that the only justified attitude with respect to the proposition that p is suspension of judgment is suspension of judgment (say that three times fast!). But consider the principle that whenever someone is committed to a proposition p they are also (perhaps implicitly) committed to the proposition that belief is the (or at least a) justified attitude towards p . Call this the “Commitment Iteration Principle”. If the Commitment Iteration Principle holds, then Pyrrhonian Skepticism is indeed self-refuting. For Pyrrhonian skeptics are committed to the claim that suspension of judgment is the only justified attitude with respect to some proposition p . By the Commitment Iteration Principle, they are then committed to the claim that belief is a justified attitude with respect to the proposition that suspension of judgment is the only justified attitude with respect to p . Therefore, if they are in addition committed to the claim that suspension of judgment is the only justified attitude with respect to that very same proposition, they are committed to an inconsistent set of propositions. But Pyrrhonian skeptics need not hold the Commitment Iteration Principle. Indeed, they are committed to thinking that suspension of judgment is the only justified attitude with respect to the Commitment Iteration Principle itself (and also with respect to analogous principles which may make trouble for Pyrrhonian Skepticism). Of course, Pyrrhonian Skepticism will not be acceptable to anyone who does hold the Commitment Iteration Principle—but neither will Pyrrhonian Skepticism be acceptable to anyone who holds that we should not suspend judgment with respect to some proposition. It is not clear, then, that the charge of self-refutation represents an independent indictment of Pyrrhonian Skepticism. In any case, contemporary philosophers find Pyrrhonian Skepticism interesting not because they take seriously the possibility of its truth, but rather because there are interesting arguments in its favor, the responses to which shape the contours of many contemporary epistemological theories.

We have distinguished between Cartesian and Pyrrhonian Skepticism, but we have characterized both views in terms of a generic field of propositions F . In the case of Pyrrhonian Skepticism, F includes every proposition, but we can generate different versions of Cartesian Skepticism by varying F . A prominent version of Cartesian Skepticism is external-world skepticism—i.e., Cartesian Skepticism with respect to any proposition about the “external world” (not about the subject’s own mind). [ 4 ] In what follows, we concentrate on external world Cartesian Skepticism and Pyrrhonian Skepticism.

3. The Argument for Cartesian Skepticism Employing the Closure Principle

Many contemporary philosophers take the canonical argument for Cartesian Skepticism to involve skeptical hypotheses and a Closure Principle (CP). [ 5 ] A skeptical hypothesis (with respect to a proposition p and a subject S ) is a proposition SH such that if SH were true, then: (a) S would not know p , and (b) S would not be able to distinguish SH from a situation where S knows p . The evil demon scenario that Descartes envisions at the end of his “First Meditation” functions as a near-universal skeptical hypothesis, for the demon has the power to deceive any subject regarding almost any proposition. One way in which a SH may satisfy (a) is by describing a situation where p is false, but this is not the only way. Descartes’ evil demon may induce in a disembodied subject’s mind an experience as of the subject’s own hands in front of her, as a result of which the subject believes that there are hands in front of her, while at the same time dangling some unattached hands in front of the subject (we are waiving here difficulties having to do with how to locate objects relative to disembodied subjects). The subject’s belief that there are hands in front of her is in that case true, but she still doesn’t know it. The connection between Closure principles and arguments for skepticism gets complicated if we countenance skeptical hypotheses which do not entail the falsehood of the proposition in question, and so in what follows we limit our discussion to those that do.

Letting “ h ” stand for any proposition about the external world we would ordinarily take ourselves to be justified in believing, for example, G. E. Moore’s famous “here’s a hand” (Moore 1939 [1993]), and re-using “ SH ” for a skeptical hypothesis relative to h (we leave the subject tacit), we can state the contemporary canonical CP-style argument for Cartesian Skepticism as follows:

• CP1. If I am justified in believing that h , then I am justified in believing that \({\sim}\textit{SH}\).
• CP2. I am not justified in believing that \({\sim}\textit{SH}\).
• Therefore, I am not justified in believing that h .

CP1 follows from the following Closure Principle (letting “ Jx ” stand for the subject is justified in believing x ):

Closure Principle [ CP ]: For all propositions x and y , if x entails y , and Jx , then Jy .

(In the argument above, \(x = h\) and \(y = {\sim}SH\).)

A crucial feature of CP is that it does not depend upon employing a stringent notion of justification. Suppose that (positive) justification comes in degrees, where the lowest degree is something like mere plausibility and the highest degree is absolute certainty. CP could be recast as follows:

CP*: For all propositions, x and y , if x entails y , and Jx to degree u , then Jy to degree v (where \(u \le v)\).

There appear to be only three ways that one can respond to the CP-style skeptical argument: deny at least one premise, deny that the argument is valid, or reluctantly accept the conclusion—if neither of the first two alternatives succeeds.

Let us begin an examination of CP1 and the general closure principle, CP, of which CP1 is an instantiation. Closure certainly does hold for some properties, for example, truth. If p is true and implies q , then q is true. It just as clearly does not hold for other properties, for example being surprising. It might be surprising that Tomás is taller than his father, but it is certainly not surprising that Tomás is taller than someone, and yet the former entails the latter. What about justified belief? Does Closure hold for it?

It might be thought that the answer must be a clear “No”, for the following reasons. First, notice that every logical truth is entailed by every proposition. If Closure held for justification, then we would have to say that everybody is justified in believing every logical truth (provided that we are willing to grant that everybody is justified in believing at least one proposition). But this doesn’t seem plausible. Some logical truths are too complicated to even parse, let alone be justified in believing. If this is true, then Closure doesn’t hold for belief (that is to say, we may fail to believe propositions entailed by propositions we already believe). The existence of very complicated logical truths also underlies another worry for Closure. For to every logical entailment between propositions there corresponds a logical truth: the (material) conditional with the entailing proposition in the antecedent and the entailed proposition in the consequent. Some of these logically true conditionals will be examples of propositions that we are not justified in believing (if only because the consequent is too complicated for beings like us to even parse). In that case, we might well be justified in believing their antecedents without being justified in believing their consequents.

But it also appears that CP can easily be repaired. We can stipulate (i) that the domain of the propositions in the generalization of CP includes only contingent propositions that are within S ’s capacity to grasp and (ii) that the entailment is “obvious” to S . The skeptic can agree to those restrictions because the skeptical scenarios are posited in such a way as to render it obvious that our ordinary beliefs are false in those scenarios, and it is taken to be a contingent claim that S is in the actual circumstances as described in the antecedent. (For a full discussion of the required repairs of CP, see David & Warfield 2008 and Hawthorne 2014.)

There is one other important, required clarification of the restricted version of CP. “Justified belief” is ambiguous. It could be used to refer to a species of actually held beliefs—namely, those actually held beliefs of S that are justified. Or it could refer to propositions that S is justified in believing—regardless of whether S does indeed believe them. Following Roderick Firth, the distinction between actually held justified beliefs and propositions one is justified in believing, regardless of whether they are actually believed, is often marked by distinguishing between doxastic and propositional justification (see Firth 1978). If CP is to be acceptable, “justified in believing” in the consequent must be used so as to refer to propositional justification for a reason already cited, i.e., that Closure does not hold for belief. In other words, one of S ’s actual beliefs, p , might be justified and S still fail to believe some proposition that is entailed by p . [ 6 ]

We are now in a position to ask: Does the restricted form of closure hold? There are at least three types of argument against closure in the literature: alleged counterexamples, alleged unpalatable consequences, and incompatibility with allegedly plausible epistemological theories. In the remainder of this section we examine one exemplar of each of these.

Fred Dretske and others have produced cases in which they believe CP fails. [ 7 ] Dretske writes:

You take your son to the zoo, see several zebras, and, when questioned by your son, tell him they are zebras. Do you know they are zebras? Well, most of us would have little hesitation in saying that we did know this. We know what zebras look like, and, besides, this is the city zoo and the animals are in a pen clearly marked “Zebras.” Yet, something’s being a zebra implies that it is not a mule and, in particular, not a mule cleverly disguised by the zoo authorities to look like a zebra. Do you know that these animals are not mules cleverly disguised by the zoo authorities to look like zebras? If you are tempted to say “Yes” to this question, think a moment about what reasons you have, what evidence you can produce in favor of this claim. The evidence you had for thinking them zebras has been effectively neutralized, since it does not count toward their not being mules cleverly disguised to look like zebras. (Dretske 1970: 1015–1016)

Dretske is speaking of knowledge rather than justified beliefs, but that seems irrelevant since the issue concerns the supposed lack of a sufficient source of evidence or reasons for the claim that the animal is not a cleverly disguised mule.

The crucial thing to note about this proposed counterexample is that it works only if the Closure Principle entails that the very same source of evidence that justifies S in believing that the animals are zebras must justify S in believing that they are not cleverly disguised mules. Since the evidence for the former has been “effectively neutralized”, it is not available for the latter. Now, in response one could claim that once the question of whether the animals are disguised mules has been raised, the evidence is “effectively neutralized” for both the former and the latter, and S is no longer justified in believing that the animals are zebras. Thus, it could be held that this example could actually be used to support CP. Nevertheless, let us grant that the evidence for the claim that the animals are zebras cannot be used to show that they are not cleverly disguised mules. Still, it could be argued that this would not force giving up CP.

Such an argument could begin by recalling that CP claimed merely that whenever a subject is justified in believing p , then that subject is justified in believing q . CP does not require that the subject have the same evidence for p as she does for q . Dretske’s purported counterexample seems to require that CP implies that the adequate source of evidence is the same for both propositions.

No doubt this constraint sometimes correctly portrays the relevant evidential relationships when some proposition entails some other proposition. For example, suppose I have adequate evidence for the claim that Anne has two brothers. Then it would seem that the very same evidence would be adequate for believing that Anne has at least one brother. But the defender of CP, and more particularly the Cartesian Skeptic, could point out that closure does not require this to hold for every case.

There are two other possibilities. First, one may hold that when p entails q and there is some evidence e for p , it is p itself that is evidence for q . For example, it may be held that given that I have adequate evidence for believing that 2 is a prime number, I can use that very proposition (that 2 is a prime number) as an adequate reason for believing that there is at least one even prime. (See Klein 1981, 1995, and 2000, but see below for reasons for doubting that this is a genuine possibility.) Second, there are cases where the order is reversed because q serves as part of the evidence for p . For example, suppose that I am justified, ceteris paribus , in believing that (pure) water is present if I am justified in believing that there is present, at standard temperature and pressure, a clear, odorless, watery-tasting and watery-looking fluid that contains hydrogen and oxygen. This pattern is typical of abductive inferences, and is often referred to as “inference to the best explanation”. (See Vogel 1990, 2014b for a discussion of Cartesian Skepticism and inference to the best explanation.) In addition, there are cases in which it seems that some contraries of h need to be eliminated prior to h ’s being justified. For example, reconsidering the zebra-in-the-zoo case, it seems to be true that if I had some good reason to think that the animals are cleverly disguised mules, such a contrary would need to be eliminated before I would be justified in believing that the animals were zebras. [ 8 ]

It could also be argued that CP has unacceptable consequences. Of course, one of those unacceptable consequences may well be Cartesian Skepticism itself, but to point that out in the present context would be dialectically unhelpful. It has been argued, however, that CP by itself has far-reaching skeptical consequences. Notice that the argument for Cartesian Skepticism under consideration contains CP2 as an essential premise. The present concern is that CP by itself (and therefore CP1, if justified on the basis of CP), without help from CP2, has skeptical consequences. If that were true, that would be a reason to be wary of CP, for it would be a much stronger principle than advertised.

The argument can be presented as a conflict between CP, on the one hand, and three other principles. Those three other principles are, allegedly, beyond reproach, and so CP is to be blamed for the conflict. The first principle in question may be thought of as enshrining the possibility of knowledge (and justification) by ampliative inference:

Ampliativity : It is possible for a subject S to be justified in believing h on the basis of evidence e even if S does not have independent justification (of at least the same degree of S ’s justification for believing h ) for believing a proposition p such that p and e together entail h .

Ampliativity would be true if, for example, we can be justified in believing the conclusion of an inductive argument (say, that all emeralds are green) on the basis of believing its premises (say, that a properly selected group of emeralds have been observed to be green), without in addition being independently justified in believing any other proposition which, together with those premises, entails the conclusion (such as, for example, the proposition that if a properly selected sample of emeralds have all been green, then all emeralds are green).

The next principle is in conflict with what we presented above as an alternative possibility to Dretske’s interpretation of the evidential structure of CP. Dretske’s counterexample works, we said, only if CP holds that whatever justifies the subject in believing p is also what justifies her in believing q . But there are two other possibilities. Maybe the evidential relation is reversed: whatever justifies us in believing q justifies us in believing p . Or maybe, we said, p itself, and not whatever justifies us in believing p , justifies us in believing q . The next principle goes directly against this possibility:

Mere Lemmas : If S is justified in believing p on the basis of some evidence e , then p itself can justify S in believing some other proposition q only if e justifies S in believing q .

We call the principle “Mere Lemmas” because the idea behind it is that if a proposition is a mere lemma, in the sense that it derives all of its justification from some prior evidence e , then it doesn’t have justificatory powers of its own, independent of the justificatory powers of e . Suppose, for instance, that you start out by knowing that Jim has a pet, but you don’t know what kind of pet it is (the example is from Pryor 2004). Then you come to know that it is a hairless pet. Now you become justified (perhaps to a small degree) in believing that Jim’s pet is a hairless dog. That is to say, whatever degree of justification you had before to believe that Jim’s pet is a hairless dog, you are now somewhat more justified in believing that same proposition. That Jim’s pet is a hairless dog of course entails that Jim’s pet is a dog. But your justification for believing that Jim’s pet is a hairless dog cannot in any way be transmuted into justification for believing that Jim’s pet is a dog. Whatever degree of justification you had before for believing that Jim’s pet is a dog, you are now less justified in believing that same proposition (because hairless dogs are a small minority of hairless pets).

But what about the example with which we introduced the idea that, sometimes, when e is evidence for p , then p itself can be evidence for q ? The example was the following: we can have adequate evidence for believing that 2 is a prime number, and then that proposition itself (that 2 is a prime number) can justify us in believing that that there is at least one even prime number. But, when examined more closely, this is not an obvious counterexample to Mere Lemmas. For, what could our adequate evidence that 2 is a prime number be? Presumably, it would be that 2 is divisible only by 1 and 2. That just is the definition of what it means for 2 to be a prime number, however, so some may balk at the idea that it counts as evidence for the proposition in question (rather than being identical with it). In any case, it would not count as a counterexample to Mere Lemmas. For if we have no evidence for the proposition that 2 is a prime number, then the condition for the application of Mere Lemmas is not satisfied. [ 9 ] If, on the other hand, our evidence is that 2 is divisible only by 1 and 2, then that proposition itself is obviously evidence for the proposition that an even number is prime.

Our final principle is the following:

Entailment : If p entails q , then q cannot justify S in disbelieving p .

The idea behind this principle is that if p entails q , then should q turn out to be true then things are as p says they are, and so we can hardly use q as evidence against p . We return to Entailment below, but first we show how these three principles are in conflict with CP.

Assume, with Ampliativity, that a subject S is justified in believing a proposition h on the basis of some evidence e without having independent justification for believing any other proposition p such that p together with e entails h . Notice that h obviously entails h or not-e . Therefore, by CP, S is justified in believing h or not-e . But, of course, e together with h or not-e entails h . Therefore, if S is justified in believing h on the basis of e , then there is a proposition which S is justified in believing and which together with e entails h .

Notice that this is close to, but not quite, the negation of Ampliativity. For Ampliativity denies that there will be any such proposition which S is independently justified in believing, and for all we have said S ’s justification for believing h or not-e is not independent. Independent of what? Of S ’s justification for believing h itself. For all we have said so far, S might be justified in believing h or not-e on the basis of h , or on the basis of e itself.

But, given Mere Lemmas, h cannot justify S in believing any proposition unless e does. Therefore, the only option left open, short of denying Ampliativity, is to argue that e itself justifies S in believing h or not-e . But that is incompatible with Entailment. For notice that for e to justify S in believing h or not-e is for e to justify S in disbelieving its negation, i.e., e and not-h . But, of course, e and not-h entails e , and so the entailment principle has it that e cannot justify S in disbelieving e and not-h —i.e., e cannot justify S in believing h or not-e .

Although this particular reconstruction is our own (for more on it, see Comesaña forthcoming), some philosophers have taken arguments similar to it to count against CP (see, for example, Huemer 2001 and Sharon & Spectre 2017, and cf. Comesaña 2017). However, others have argued against Entailment (see, for example, Pryor 2014a,b and Vogel 2014b), and yet others have argued that denying Ampliativity itself is not as absurd as it might sound (Comesaña 2014a,b). The argument cannot, therefore, be taken to be a conclusive blow against CP.

Finally, some epistemological theories are in conflict with CP. [ 10 ] Robert Nozick’s account of knowledge is the best such example. Roughly his account is this (Nozick 1981: 172–187):

S knows that p iff :

• S believes p ;
• if p were true, S would believe p ;
• if p were not true, S would not believe p .

Nozick called his account a “tracking” account of knowledge because whenever S knows that \(p, S\)’s beliefs track p . Think of a guided missile tracking its target. If the target were to move left, the missile would move left. If the target were not to move left, the missile would not move left. According to the tracking account of knowledge our beliefs must track the truth if we are to have knowledge.

There is one important clarification of conditions 3 and 4 that is discussed by Nozick, namely, that the method by which S acquires the belief must be held constant from the actual world to the possible world. A doting grandmother might know that her grandchild is not a thief on the basis of sufficiently good evidence, but would still believe that he wasn’t a thief, even if he were, because she loves him. So, we must require that the grandmother use the same method in both the actual and the near possible worlds, for, otherwise, condition (4) would exclude some clear cases of knowledge. This is not the place to provide a full examination of Nozick’s account of knowledge. [ 11 ] What is crucial for our discussion is that it is easy to see that, if Nozick’s account is correct, closure will fail for knowledge in just the kind of case that the Cartesian Skeptic is putting forward because of condition (4). Suppose S knows that there is a chair before her. Would she know that she is not in a skeptical scenario in which it merely appears that there is a chair? If the fourth condition were a necessary condition of knowledge, she would not know that because if she were in such a scenario, she would be fooled into thinking that she wasn’t. Thus, either condition (4) is too strong or CP fails.

There are some reasons for thinking that condition (4) is too strong. Consider, for instance, this case in the literature: You put a glass of ice-cold lemonade on a picnic table in your backyard. You go inside and get a telephone call from a friend and talk for half an hour. When you hang up you remember that you had left the ice-cold lemonade outside exposed to the hot sun and come to believe that it isn’t ice-cold anymore. It would seem that you could know that. Indeed, if it were false, that could only be due to some bizarre circumstance. Thus, if the lemonade were still ice-cold, you would believe that it wasn’t (see Vogel 1987: 206). The moral of this (and similar) cases seems to be that sensitivity is not a correct condition on knowledge.

There is much more to say about CP and CP1, but we will move on to considering the argument’s other premise.

CP2 claims that we are not justified in denying the skeptical hypothesis—in other words, that we are not justified in believing that we are not being deceived. What arguments can be given for CP2? It is tempting to suggest something like this: The skeptical scenarios are developed in such a way that it is assumed that we could not tell that we were being deceived. For example, we are asked to consider that there is an Evil Genius “so powerful” that it could (1) make me believe that there were hands when there were none and (2) make it such that I could not detect the illusion. But the skeptic must be very careful here. She cannot require that in order for S to know (or be justified in believing) something, say x , that if x were false, she would not still believe x . We have just seen (while examining Nozick’s account of knowledge) that this requirement is arguably too strong. So the mere fact that there could be skeptical scenarios in which S still believes that she is not in such a scenario cannot provide the skeptic with a basis for thinking that she fails to know that she is not (actually) in a skeptical scenario. But even more importantly, were that a requirement of knowledge (or justification), then we have seen that closure would fail and, consequently, the basis for the first premise in the CP-style argument for Cartesian Skepticism would be forfeited. [ 12 ]

Ernest Sosa has argued for three interrelated theses regarding CP2 and Nozick’s sensitivity condition: (i) that sensitivity can be easily confused with a different condition on knowledge (which Sosa calls safety); (ii) that while sensitivity is not a correct necessary condition on knowledge, safety is; (iii) finally, that our belief in the negation of skeptical hypotheses is safe despite being insensitive. [ 13 ]

Nozick’s sensitivity condition is a subjunctive conditional : if p were false, S would not believe it. The usual way in which such conditionals are evaluated is by assuming that there is an ordering of possible worlds according to how much they resemble the actual world. A subjunctive conditional \(A \rightarrow B\) is true if and only if B is true in the closest (or all the closest) possible worlds where A is true. According to this semantics, subjunctive conditionals do not contrapose (the contrapositive of a conditional if A, B is if not-B, not-A ). Thus, suppose that we flip a coin to decide whether you or I will strike this match: heads you strike it, tails I do. The coin comes up head, you strike the match and it lights. In this situation, it is true that if I had struck the match, it would have lit. But it doesn’t seem to be true that if the match hadn’t lit then I wouldn’t have struck it. The match might have failed to lit because it was wet while either of us struck it. In the possible worlds terminology, the closest possible world where I strike the match is a world where it lights, but there are possible worlds where the match doesn’t light and I strike it that are as close to actuality as are worlds where the match doesn’t light and you strike it.

After noticing the failure of subjunctives to contrapose, Sosa proposed that we should replace Nozick’s sensitivity condition with its contrapositive, which Sosa calls a ‘safety’ condition. The following formulation seems to capture Sosa’s intent:

Safety : S ’s belief that p based on e is safe if and only if S would not easily believe that p based on e without it being so that p (in symbols, S believes that p on basis \(e \rightarrow p\)). (Sosa 2002) [ 14 ]

Now, one initial worry about safety as a condition on knowledge is that, given that belief and truth are also necessary for knowledge, safety will always be (in this context) a true-true conditional (that is to say, both its antecedent and consequent will be true). This means that Sosa cannot accept the possible worlds semantics for subjunctive conditionals briefly sketched above, at least if we assume that every world is closer to itself than any other word. For when we have a true-true conditional, the closest world where the antecedent is true will be the actual world, and so every such conditional will be true (and, hence, any condition formulated by such conditionals will be trivially satisfied). [ 15 ] Rather, Sosa understands the truth-conditions for the relevant conditions as requiring that the consequent be true in all nearby possible worlds where the antecedent is true.

Sosa’s idea, then, is that we can explain away the temptation to think that CP2 is true by noticing that although safety and sensitivity are easily confused with one another, my belief that I am not the victim of a skeptical scenario is insensitive but safe, and that whereas sensitivity is not a condition on knowledge, safety is.

But is safety a condition on knowledge? Several authors have thought that, just as there are counterexamples to sensitivity, there are counterexamples to safety as well. Here is one (taken from Comesaña 2005b):

Halloween Party : There is a Halloween party at Andy’s house, and I am invited. Andy’s house is very difficult to find, so he hires Judy to stand at a crossroads and direct people towards the house (Judy’s job is to tell people that the party is at the house down the left road). Unbeknownst to me, Andy doesn’t want Michael to go to the party, so he also tells Judy that if she sees Michael she should tell him the same thing she tells everybody else (that the party is at the house down the left road), but she should immediately phone Andy so that the party can be moved to Adam’s house, which is down the right road. I seriously consider disguising myself as Michael, but at the last moment I don’t. When I get to the crossroads, I ask Judy where the party is, and she tells me that it is down the left road.

That case is a counterexample to safety insofar as we agree that I know that the party is at the house down the left road, and yet it could very easily have happened that I have that same belief on the same basis without it being so that the belief was true.

So far, we have argued that there are dangers in defending CP2 by appealing to the sensitivity condition, and that Sosa’s attack on CP2 might itself be subject to doubt. What else can be said for or against CP2?

Let’s go back to the rough idea that there is some kind of epistemic symmetry between what we take to be the actual case and a skeptical scenario. Of course, if we were the victims in a skeptical scenario, we wouldn’t know that we are not (if only because it would be false, but perhaps not only because of that). Given symmetry, even if we are not victims of a skeptical scenario, we do not know that we are not. Moreover, we know all of this. As we suggested in section 1, if we know that we don’t know that p , then we are not even justified in believing that p . Therefore, CP2. Every step in this argument can be challenged, but there is no doubt that many philosophers find something along these lines at least worth thinking about. Let us take a closer look at the first step, the claim that there is an epistemic symmetry between the good case and the skeptical scenario.

What can this alleged symmetry amount to? One idea is that we have the same evidence in both cases. According to a Cartesian account of this common evidence, it consists in mental states of the subject, such as her experiences. By construction, the subject has the same experiences in the skeptical scenario as she does in the good case. But some philosophers, most notably Williamson 2000, have denied that we have the same evidence in the good and the skeptical case. According to Williamson, our evidence is constituted not by our experiences, but by what we know. Given that in the good case we know more propositions that in the bad case, we have more evidence in the good case than we do in the skeptical case. In the good case, for instance, we know mundane propositions such as the proposition that we have hands. Given that knowledge entails justification, in the good case we are justified in believing that we have hands. Given CP, in the good case we are justified in believing that we are not in the skeptical case. This account of evidence entails that the relation of indiscriminability between the good case and the skeptical case is not symmetric: victims of a skeptical scenario cannot distinguish the skeptical scenario from the good case (for all they know, they are in the good case, and for all they know, they are in the skeptical case), but subjects in the good case can distinguish between the cases (they know that they are in the good case, and—again, given CP—they know that they are not in the skeptical case). [ 16 ]

But even those contemporary philosophers who grant that our epistemic position with respect to external world propositions is the same in the normal case as in the skeptical scenario can object to the symmetry thesis. For even granting (as we must) that in the skeptical scenario we do not know that we are not in the skeptical scenario, it doesn’t follow that in the ordinary case we do not know that we are not in the skeptical scenario, not even assuming that we have the same evidence in both cases. To begin with, an obvious difference between the normal case and the skeptical scenario is that in the skeptical scenario the proposition in question (that we are not in the skeptical scenario) is false, whereas in the normal case it is true. Given that knowledge requires truth, we can explain why we lack knowledge in the skeptical scenario by appealing to this truth condition on knowledge, rather than to the paucity of our evidence. In other words, our evidence for thinking that we are not in the skeptical scenario, this reply holds, is good enough to know that proposition, if only it were true. Now, the skeptic can then reply that not all skeptical scenarios are such that external worlds propositions are false in them. For instance, if I am right now dreaming that I have hands I do not thereby know that I have hands, even though I do have hands while dreaming. We noted above that the introduction of skeptical hypotheses which do not entail the falsity of external world propositions complicates the CP argument, but let us here bracket that issue. For, in addition to truth, knowledge plausibly requires other non-evidential conditions. In the wake of the Gettier problem, for instance, many philosophers have accepted that besides belief, justification and truth, the right kind of relation between the truth of the proposition and the belief must hold, and arguably it is this that fails in the dreaming scenario, rather than (again) the paucity of our evidence (see entry on the analysis of knowledge ). Therefore, it can be held that there is an asymmetry between the good case and the skeptical scenario even if we grant that we have the same evidence in both cases.

The Cartesian skeptic can nevertheless raise an uncomfortable question at this point: what is this alleged evidence in favor of the proposition that we are not in a skeptical scenario? One tempting answer is that the evidence in question consists precisely of those external world propositions which are the target of the Cartesian argument. I know that I have hands, and, according to this view, that very proposition is my evidence for the proposition that I am not a handless brain in a vat. But recall our discussion of Dretske’s mule case. There we pointed out that Dretske is, in effect, assimilating Closure and Transmission principles—i.e., assuming that the only way in which Closure principles can hold is if some evidence e is evidence both for p and any q entailed by p . We noted then that there is at least another possibility: it might be that we must be antecedently justified in believing q in order to be justified in believing some p which entails it. And indeed, it seems plausible that this is the direction of the evidential relation between external world propositions and the negation of skeptical hypotheses: we cannot be justified in believing external world propositions unless we have antecedent justification for believing the negation of skeptical hypotheses (but cf. Pryor 2000).

Another alternative is to say that no evidence justifies us in believing the negations of skeptical hypotheses, but that we are nevertheless justified in believing them. On one version of this view, put forward by Crispin Wright 2004, our entitlement to accept that we are not in a skeptical scenario does not depend on our having any kind of evidence, either empirical or a priori (see also Coliva (2015) for a development of a view in this neighborhood). Indeed, we are entitled to accept those propositions because unless we were we would not be justified in believing any proposition. Notice two important terminological points in the statement of Wright’s view: he doesn’t think that we are justified in believing that we are not in a skeptical scenario, but that we are entitled to accept that proposition. What are the differences between justification and entitlement, on the one hand, and belief and acceptance, on the other? Roughly, what we are calling justification Wright calls “warrant”. He thinks that there are two kinds of warrant: we can be warranted in believing a proposition because we have an evidential justification for it (where the evidence consists of the propositions we are warranted in believing or accepting), or we can be entitled to accept it even in the absence of any justification for them. As for the difference between belief and acceptance, Wright is prepared to grant that to count as a belief an attitude must be evidence-based, and so entitlements cannot be entitlements to believe. To be entitled to accept a proposition, for Wright, is to be justified in behaving (where “behavior” is understood broadly, to include cognitive inferential behavior, for instance) approximately as one would if one believed the proposition.

On another version of the view, although we do not have empirical evidence for the proposition that we are not in a skeptical scenario, we do have a kind of justification for it which does not rest exclusively on the fact that if we didn’t then we wouldn’t be justified in believing anything. Stewart Cohen 2010 has argued that our justification for believing that we are not in a skeptical scenario derives from the rationality of certain inferential rules (see also Wedgwood 2013). One such rule justifies us in concluding (defeasibly) that there is something red in front of us if we have an experience with the content that there is something red in front of us. Now, we can use that rule “online”, when we do in fact have an experience with the content that there is something red in front of us, or “offline”, assuming for the sake of argument that we have an experience with the content that there is something red in front of us to see what follows from it. According to the rule in question, it follows (again, defeasibly) that there is something red in front of us. We can now cancel the assumption by concluding (defeasibly) with the following conditional: if I have an experience with the content that there is something red in front of me, then there is something red in front of me. Notice that this conditional is incompatible with one specific skeptical hypothesis: the hypothesis that (for whatever reason) I have an experience with the content that there is something red in front of me but there is nothing red in front of me.

So far, we have looked at reasons for and against the two premises of the CP argument for Cartesian Skepticism. A different kind of approach to the argument requires some setup. Philosophers routinely distinguish between sentences and the propositions expressed by some of them. Sentences are language-dependent entities whereas propositions are (something like) the informational content of some of those language-dependent entities (see entry on propositions ). Thus, we distinguish between the proposition that it is raining and the English sentence It is raining . That very same proposition can be expressed by other sentences, such as the Spanish sentence Está lloviendo . Moreover, which proposition a given sentence expresses (if any) can depend on contextual factors—that is to say, the same sentence may express one proposition when produced in a given a context, and a different one when produced in a different context. Thus, when Tomás says that it is raining he expresses the proposition that it is raining in Tucson on May 14, 2019, whereas when Manolo said “Está lloviendo” last week, he expressed the proposition that it was raining in Mar del Plata on May 10, 2019.

The contextualist response to the argument for Cartesian Skepticism rests on the claim that which propositions the sentences used in that argument express is also a context-sensitive matter. Different contextualists would fill in the details in different ways—here we follow most closely the contextualism of Cohen 1987, 1988, 2000, 2005, 2014a,b, but see also Lewis 1996, DeRose 1992, 1995, 2002, 2004, 2005 and Stine 1976. Notice, to begin with, that justification comes in degrees: one can be more justified in believing one proposition than another. But there is also such a thing as being justified tout court . In this respect, it can be argued that “justified” is like “tall”, in that we can make sense both of comparative uses, such as when we say that Tomás is taller than his mother, and of non-comparative ones, such as when we say that Jordan is tall. Notice also that which proposition is expressed by a non-comparative use of “tall” does not float free from what would be appropriate comparative uses. Thus, when I say “Jordan is tall”, what I say is true provided that Jordan is taller than the average subject in the relevant contrast class. Thus, if Jordan is a fifth-grader, then what I said would be true if Jordan is taller than the average fifth-grader, whereas if Jordan is an NBA player, then what I said would be true if Jordan is taller than the average NBA player (who plays in Jordan’s position, perhaps). Similarly, the contextualist claims that when I say that I am justified in believing a proposition, what I say is true if and only if my degree of justification for believing the proposition is higher than a contextually set threshold. That threshold, moreover, can vary with the conversational context. Thus, if we are doing epistemology and thinking about the requirements for justification, the threshold required for an utterance of “I am justified in believing I have hands” goes up to the point where few (if any) of us would count as having said something true, whereas in an everyday context the threshold goes down to the point where most of us would count as having said something true.

According to contextualism, then, there is no single proposition expressed by the sentences used in the CP-based argument for Cartesian Skepticism. Rather, there are many such propositions. Two interesting ones are the propositions expressed in everyday contexts, where CP2 as well as the conclusion of the argument express false propositions, and those expressed in heightened-scrutiny contexts, where both CP2 as well as the conclusion of the argument express true propositions. CP1 (as well as CP itself) always expresses a true proposition, as long as we do not change contexts mid-sentence. Thus, the contextualist response to the CP-based argument is that it is at least two arguments: a sound one, when produced in heightened-scrutiny contexts, and one with a false premise (and a false conclusion) when produced in ordinary contexts. Contextualism is thus a more concessive response to the skeptic than the ones we have canvassed so far, for it concedes that the sentences used in the argument for Cartesian Skepticism can be used to express propositions which constitute a sound argument.

But even though Contextualism represents a concessive answer to skepticism, it is certainly not concessive enough in the eyes of the skeptic. For the contextualist simply asserts that, in ordinary contexts, we are justified in rejecting skeptical hypotheses. But recall that the skeptic’s idea was that CP2 is true even when we have in mind even minimally demanding standards for justification. In other words, the skeptic claims that we are not justified in believing the negation of skeptical hypotheses even a little bit, not just that we do not meet a very stringent standard for justification. Now, the skeptic might well be wrong about this, but the contextualist, qua contextualist, does not have any argument for his trademark claim that we do have some justification for believing the negation of skeptical hypotheses. In this respect, contextualism as a response to the skeptic is parasitic on some independent argument to the effect that we do have that kind of justification.

A related issue regarding Contextualism pertains to its relevance to skepticism. Grant, if only for the sake of argument, that Contextualism regarding knowledge and justification attributions is true. That is to say, grant that there are multiple properties that, say, “justified” could refer to. Couldn’t skeptics, and epistemologists more generally, be interested in a subset (perhaps just one) of them? If so, the interesting epistemological arguments would pertain to the conditions under which that property is instantiated, and Contextualism would fall by the wayside. For a debate regarding this and related issues, see Conee 2014a,b and Cohen 2014a,b.

A view which is related to, but crucially different from, Contextualism goes under various names in the literature: “Subject-Sensitive Invariantism”, “Interest Relative Invariantism” or views which admit of “pragmatic encroachment” (see Fantl and McGrath 2002, 2007, 2009; Hawthorne 2003; and Stanley 2005). Whereas the contextualist thinks that the same sentence attributing justification can express different propositions depending on the context in which it is produced, the subject-sensitive invariantist thinks that the proposition expressed is invariant, but its truth-value depends on features of the subject which can vary (such as how important it is to the subject that the belief in question be true). Very roughly, a version of subject-sensitive invariantism has it that a sentence of the form “ S is justified in believing p ” invariantly expresses a proposition which entails that S ’s justification for believing p is at least high enough for S to be rational in acting as if p is true. Notice that whether it is rational for S to act as if p is traditionally thought to depend on two things: the degree of justification S has for believing that p (or, perhaps more commonly in the context of decision theory, which degree of belief, or credence, S is justified in assigning to p ), and S ’s preferences. Thus, the more sensitive S ’s preferences are with respect to whether p is true, the more justified in believing p S must be for the proposition that S is justified ( tout court ) in believing p to be true. For instance, if nothing much hangs, for S , on whether there is orange juice in the house, a faint memory of having seen some in the fridge might be enough for it to be true that S is justified in believing that there is orange juice in the house. On the other hand, if S is diabetic and needs to ingest some sugar quickly, that same faint memory might not be enough for that same proposition to be true. Notice the difference between Contextualism and Subject-Sensitive Invariantism: the contextualist might say that the same sentence (that S is justified in believing that there is orange juice in the house) expresses two different propositions (one true, the other false) depending on whether the conversational context includes the information that S is diabetic and needs to ingest sugar; the subject-sensitive invariantist, on the other hand, holds that the sentence in question always expresses the same proposition, but that very proposition is true in the first case but false in the second.

Subject-Sensitive Invariantism has been subject to a number of criticisms (see McGrath 2004; DeRose 2002, 2004, 2005; Cohen 2005; Comesaña 2013; Anderson and Hawthorne, 2019a,b), but the general approach has also been ably defended (see the previously cited work by Fantl and McGrath). Nevertheless, the same issue that arose with respect to Contextualism seems to arise here. The Subject-Sensitive Invariantist needs an independent argument to the effect that we can be justified at least to a minimal degree in believing the negations of skeptical hypotheses, for otherwise his trademark claim that propositions attributing us justification for believing such claims are true is itself unjustified.

5. Pyrrhonian Skepticism

We turn now to Pyrrhonian Skepticism. [ 17 ] We remind the reader that our main interest here is not historical (for which see the entry on ancient skepticism ), but rather systematic: we want to canvass the legacy of Pyrrhonian Skepticism for contemporary epistemology, and in so doing we set aside even the most cursory exegetical interest.

Recall that, according to Pyrrhonian Skepticism, suspension of judgment is the only justified attitude with respect to any proposition (yes, including the proposition that suspension of judgment is the only justified attitude with respect to any proposition). We are interested here in whether there are good arguments for such a view. We begin by recalling the tri-partite distinction between belief, disbelief and suspension of judgment. If we identify disbelief in a proposition with belief in its negation, then we are left with two attitudes within the realm of coarse-grained epistemology: belief and suspension of judgment. We assume also that the arguments to follow are addressed to someone who has an interest in, and has considered, the propositions in question. Otherwise, there is always the possibility of not taking any attitude whatsoever towards a proposition. Such lack of an attitude cannot itself be (epistemically) justified or not. But if the subject is to take an attitude, then the argument for Pyrrhonian Skepticism has it that suspension of judgment is the only justified one.

The Pyrrhonian skeptics sought suspension of judgment as a way of achieving calm ( ataraxia ) in the face of seemingly intractable disagreement. The Pyrrhonians had a number of ways, or “modes”, to induce suspension of judgment. The importance of Pyrrhonian Skepticism to contemporary epistemology derives primarily from these modes, and in particular from a subset of them referred to collectively as “the modes of Agrippa”. There are five modes associated with Agrippa, but three of them are the most important: the mode of hypothesis (or unsupported assertion), the mode of circularity (“reciprocal”), and the mode of regression to infinity. The three modes of Agrippa function together in the following way. Whenever the dogmatist (Sextus refers to those who are not skeptics as “dogmatists”, and we will follow him in this) asserts his belief in a proposition \(p_1\), the Pyrrhonian will challenge that assertion, asking the dogmatist to justify \(p_1\), to give reasons for thinking that it is true. The dogmatist will then either decline to answer the challenge or adduce another proposition \(p_2\) in support of \(p_1\). If the dogmatist refuses to answer the challenge, the Pyrrhonian will be satisfied that the only justified attitude to take with respect to \(p_1\) is to suspend judgment, because no reason for it has been given (thus appealing to the mode of hypothesis). If the dogmatist adduces another proposition \(p_2\) in support of \(p_1\), then either \(p_2\) will be identical to \(p_1\) or it will be a different proposition. If \(p_2\) is the same proposition as \(p_1\), then the Pyrrhonian will also suspend judgment with respect to \(p_1\), because no proposition can support itself (thus appealing to the mode of circularity). If, on the other hand, \(p_2\) is different from \(p_1\), then the Pyrrhonian will ask the dogmatist to justify his assertion of \(p_2\). And now either the dogmatist offers no reason in support of \(p_2\), or offers \(p_2\) itself or \(p_1\) as a reason, or adduces yet another proposition \(p_3\), different from both \(p_1\) and \(p_2\). If the dogmatist offers no reason for \(p_2\), then the Pyrrhonian will invoke the mode of hypothesis again and suspend judgment in accordance with it; if either \(p_2\) itself or \(p_1\) are offered as reasons to believe in \(p_1\), then the Pyrrhonian will invoke the mode of circularity and suspend judgment in accordance with it (because not only can no proposition be a reason for believing in itself, but also no genuine chain of reasons can loop); and, finally, if the dogmatist offers yet another proposition \(p_3\), different from both \(p_1\) and \(p_2\), as a reason to believe \(p_2\), then the same three possibilities that arose with respect to \(p_2\) will arise with respect to \(p_3\). The dogmatist will not be able to continue offering different propositions in response to the Pyrrhonian challenge forever—eventually, either no reason will be offered, or a proposition that has already made an appearance will be mentioned again. The Pyrrhonian refers to this impossibility of actually offering a different proposition each time a reason is needed as “the mode of infinite regression”. The three Pyrrhonian modes, then, work in tandem in order to induce suspension of judgment with respect to any proposition whatsoever.

The Pyrrhonian use of the three modes of Agrippa in order to induce suspension of judgment can be presented in the form of an argument, which has been called “Agrippa’s trilemma”. It is at least somewhat misleading to present the Pyrrhonian position in terms of an argument, because when someone presents an argument they are usually committed to the truth of its premises and its conclusion, whereas Pyrrhonian skeptics would suspend judgment with respect to them. Nevertheless, presenting an argument for Pyrrhonian Skepticism doesn’t do much violence to this skeptical position, because what is important is not whether the Pyrrhonians themselves accept the premises or the validity of the argument, but rather whether we do. If we do, then it seems that we ourselves should be Pyrrhonian skeptics (and if we do become Pyrrhonian skeptics as a result of this argument, we can then start worrying about what to do with respect to the fact that an argument whose premises we believed—and perhaps still believe—to be true convinced us that we are not justified in believing anything). If we do not think that the argument is sound, then we stand to learn something interesting about the structure of an epistemological theory—because each of the premises of the apparently valid argument looks plausible at first sight.

Before presenting a reconstruction of Agrippa’s trilemma we need to introduce some definitions. Let’s say that a belief is inferentially justified if and only if it is justified (at least in part) in virtue of its relations to other beliefs. A justified basic belief , by contrast, is a belief that is justified but not in virtue of its relations to other beliefs. An inferential chain is a set of beliefs such that every member of the set is allegedly related to at least one other member by the relation “is justified by”. Agrippa’s trilemma, then, can be presented thus:

• If a belief is justified, then it is either a basic justified belief or an inferentially justified belief.
• There are no basic justified beliefs.
• If a belief is justified, then it is justified in virtue of belonging to an inferential chain.
• All inferential chains are such that either (a) they contain an infinite number of beliefs; or (b) they contain circles; or (c) they contain beliefs that are not justified.
• No belief is justified in virtue of belonging to an infinite inferential chain.
• No belief is justified in virtue of belonging to a circular inferential chain.
• No belief is justified in virtue of belonging to an inferential chain that contains unjustified beliefs.
• There are no justified beliefs.

Premise 1 is beyond reproach, given our previous definitions. Premise 2 is justified by the mode of hypothesis. Step 3 of the argument follows from premises 1 and 2. Premise 4 is also beyond reproach—the only remaining possible structure for an inferential chain to have is to contain basic justified beliefs, but there are none of those according to premise 2. Premise 5 is justified by appeal to the mode of infinite regression, and premise 6 is justified by appeal to the mode of circularity. Premise 7 might seem to be a truism, but we will have to take a closer look at it.

It is interesting to note that Agrippa’s trilemma is perfectly general; in particular, it applies to philosophical positions as well as to ordinary propositions. In fact, when Agrippa’s trilemma is applied to epistemological theories themselves, the result is what has been called “the problem of the criterion” (see Chisholm 1973).

Many contemporary epistemological positions can be stated as a reaction to Agrippa’s trilemma. In fact, all of premises 2, 5, 6 and 7 have been rejected by different philosophers at one time or another. We examine those responses in what follows.

Foundationalists claim that there are basic justified beliefs—beliefs that are justified but not in virtue of their relations to other beliefs. In fact, according to foundationalism, all justified beliefs are either basic beliefs or are justified (at least in part) in virtue of being inferentially related to a justified belief (or to some justified beliefs). This is where foundationalism gets its name: the edifice of justified beliefs has its foundation in basic beliefs.

But how do foundationalists respond to the mode of hypothesis? If a belief is not justified by another belief, then isn’t it just a blind assertion? If basic beliefs are justified but not by other beliefs, then how are they justified? What else besides beliefs is there that can justify beliefs?

To this last question, many foundationalists reply: experience (we are talking here about empirical knowledge; a priori knowledge raises interesting problems of its own, and it is of course also subject to Agrippa’s trilemma). To a rough first approximation that glosses over many important philosophical issues, experiences are mental states that, like beliefs, aim to represent the world as it is, and, like beliefs too, can fail in achieving that aim—that is, experiences can misrepresent. Nevertheless, experiences are not to be identified with beliefs, for it is possible to have an experience as of, e.g., facing two lines that differ in length without having the belief that one is facing two lines that differ in length—a combination of mental states that anyone familiar with the Müller-Lyer illusion will recognize.

There are three important questions that any foundationalist has to answer. First, what kinds of beliefs do experiences justify? Second, how must inferentially acquired beliefs be related to basic beliefs in order for them to be justified? Third, in virtue of what do experiences justify beliefs?

With respect to the first question, we can distinguish between traditional foundationalism and moderate foundationalism. Traditional foundationalists think that basic beliefs are beliefs about experiences, whereas moderate foundationalists think that experience can justify beliefs about the external world. Take, for example, the experience that you typically have when looking at a tomato under good perceptual conditions—an experience that, remember, can be had even if no tomato is actually there. [ 18 ] A moderate foundationalist would say that that experience justifies you in believing that there is a tomato in front of you. The traditional foundationalist, on the other hand, would say that the experience justifies you only in believing that you have an experience as of a tomato in front of you. You may well be justified in believing that there is a tomato in front of you, but only inferentially.

A traditional argument in favor of traditional foundationalism relies on the fact that whereas you can be mistaken regarding whether there is a tomato in front of you when you have an experience as of facing a tomato, you cannot, in the same situation, be mistaken regarding whether you are undergoing such an experience. From the point of view of traditional foundationalism, this fact indicates that the moderate foundationalist is taking an unnecessary epistemic risk—the risk of having a foundation composed of false beliefs.

The moderate foundationalist can reply that the traditional foundationalist must undertake a similar risk. For, while it is true that if one is undergoing a certain experience then one cannot be mistaken in thinking that one is undergoing that experience, one can still be mistaken about one’s experiences—for instance, perhaps one can believe that one is in pain even if the experience that one is undergoing is actually one of feeling acutely uncomfortable. And if it were just as difficult to distinguish between the true and the false in the realm of beliefs about our own experiences as it is in the realm of beliefs about the external world, then we could be wrong about which of our own beliefs are basically justified and which are not. If this kind of meta-fallibilism is accepted, then why not accept the further kind according to which basic justified beliefs can be false? Of course, the resolution of this dispute depends on whether, as the moderate believes, we can be mistaken about our own experiences.

What about our second question: how must basic beliefs be related to inferentially justified beliefs? Here too there are two different kinds of foundationalism: deductivism and non-deductivism. According to the deductivist, the only way in which a (possibly one-membered) set of basic justified beliefs can justify another belief is by logically entailing that other belief. In other words, there has to be a valid argument at least some of whose premises are basic justified beliefs [ 19 ] and whose conclusion is the inferentially justified belief in question. Given that the argument is valid, the truth of the premises guarantees the truth of the conclusion—it is impossible for all the premises to be true while the conclusion is false. Non-deductivism allows relations other than logical entailment as possible justificatory relations. For instance, many foundationalists will claim that good inductive inferences from basic justified beliefs provide their conclusions with justification—even though inductive arguments are not valid, that is, even though it is possible for all the premises of a good inductive argument to be true while its conclusion is false. Although these are independent distinctions, traditional foundationalists tend to be deductivists, whereas moderate foundationalists tend to be non-deductivists. Notice that for a traditional, deductivist foundationalist, there cannot be false justified beliefs. Many contemporary epistemologists would shy away from this strong form of infallibilism, and take that consequence to be an argument against the conjunction of traditional foundationalism and deductivism.

The question that is most interesting from the point of view of Pyrrhonian Skepticism is our third one: what is it about the relation between an experience and a belief that, according to the foundationalist, allows the former to justify the latter? (Analogous questions apply to non-foundationalist positions too, and the discussion to follow is not restricted to the specific case of foundationalism.) There are three different proposals about how to answer this question that are the most prominent. Let’s call the principles that assert that a subject is justified in having a certain belief given that she is undergoing a certain experience, “epistemic principles”. Our third question can then be stated as follows: what makes epistemic principles true?

The first proposal, which we shall call “primitivism”, claims that the question cannot have an intelligible answer. There is no more basic fact in virtue of which epistemic principles obtain. They describe bedrock facts, not to be explained in terms of anything else, but are instead to be used to explain other facts. Epistemological theorizing, according to the primitivist, ends with the discovery of the correct epistemic principles (for views along these lines, see Chisholm 1966 [and also the second and third editions: 1977, 1989] and Feldman & Conee 1985).

The other two positions are non-primitivist. Internalist non-primitivism holds that epistemic principles are true in virtue of facts about ourselves—for instance, one prominent internalist view is that which epistemic principles are true for a given subject is determined by which epistemic principles that subject would accept under deep reflection (see Foley 1993). [ 20 ] Externalist non-primitivism holds that epistemic principles are true in virtue of facts that are not about ourselves—for instance, one prominent externalist view is that certain experiences provide justification for certain beliefs because the obtaining of those experiences is reliably connected to the truth of those beliefs (that is, it couldn’t easily happen that those experiences obtain without those beliefs being true; see Goldman 1979).

Both externalists and internalists think that primitivists are overlooking real facts, whereas primitivists think that there are fewer things in heaven and earth than are dreamt of in non-primitivist philosophy. Within the non-primitivist camp, externalists think that internalists have too subjective a conception of epistemology—to some extent, thinking it so, or being disposed to think it so under conditions of deep reflection, makes it so for the internalist. Internalists, for their part, are likely to think that externalists are no longer engaged in the same project that both skeptics and internalist epistemologists are engaged in, the project of determining “from the inside” whether one’s beliefs are justified or amount to knowledge, because the obtaining of a relation between a belief of his and the external world is something that the subject is in no position to ascertain “from the inside”.

Infinitism, the claim that infinite evidential chains can provide justification to their members, is the answer to Agrippa’s trilemma that has received the least attention in the literature. This is due, at least in part, to the fact that infinitism has to deal with what might seem like formidable obstacles. For instance, it seems that no one actually has an infinite number of beliefs. To this objection, the infinitist is likely to reply that actually occurring beliefs are not needed, only implicit beliefs that are available to the subject in order to continue constructing his inferential chain if called upon to do so (by others or by himself). The plausibility of this reply depends on whether good sense can be made of the notion of implicit belief and the notion of an implicit belief’s being available for a subject. A second apparently formidable problem for infinitism has to do with the fact that the mere appeal to a new belief, regardless of its epistemic status, cannot provide justification to the belief we started out with. In other words, infinitism seems to run afoul of the following principle:

Principle of inferential justification : If S is justified in believing p on the basis of S ’s belief that q , then S is justified in believing q .

The infinitist might reply that he does not run afoul of that principle, because the beliefs adduced in support of the initial beliefs are themselves justified by beliefs further down the chain. But what goes for the initial set of beliefs goes, it seems, for longer chains. If the appeal to a single unjustified belief cannot do any justificatory work of its own, why would appealing to a large number of unjustified beliefs do any better? Even leaving that problem aside, the infinitist, like the coherentist, maintains that justification can arise merely in virtue of relations among beliefs. Infinitists will then have to respond to many of the same objections that are leveled against coherentism—in particular, they would have to respond to the isolation objection mentioned in the next section. (See Aikin 2011 and Klein 1999, 2007 for defenses of infinitism; and see Turri & Klein 2014; Aikin & Peijnenburg 2014; and Peijnenburg & Wenmackers 2014 for collections of essays which defend or criticize various forms of infinitism.)

Coherentists reject two related features of the picture of evidential reasons that underlies Agrippa’s trilemma. The first feature is the idea that justification is an asymmetrical relation: if a belief \(p_1\) justifies a different belief \(p_2\), then \(p_2\) does not justify \(p_1\). The second feature is the idea that the unit of justification is the individual belief. Putting these two rejections together, the coherentist believes that justification is a symmetrical and holistic matter. It is not individual beliefs that are justified in the primary sense of the word, but only complete systems of beliefs—individual beliefs are justified, when they are, in virtue of belonging to a justified system of beliefs. The central coherentist notion of justification is best taken to be a comparative one: a system of beliefs B1 is better justified than a system of beliefs B2 if and only if B1 has a greater degree of internal coherence than B2. One crucial question that coherentists have to answer, of course, is what it takes for one system of beliefs to have a greater degree of coherence than another. Many coherentists have thought that explanatory relations will be crucial in elucidating the notion of coherence: the more explanatorily integrated a system is, the more coherence it displays (see Quine & Ullian 1970 [1978] and BonJour 1978).

The main objection that coherentists have to answer has been called “the isolation objection”. The objection centers on the fact that, according to the coherentist, the justification of a system of beliefs is entirely a matter of relations among the beliefs constituting the system. But this runs against the strong intuition that experience has a very important role to play in the justification of beliefs. To illustrate the problem, suppose that you and I both have a highly coherent set of beliefs—your system, it is safe to assume, contains the belief that you are reading, whereas mine doesn’t, and it contains instead the belief that I am swimming (because, let us suppose, I am swimming right now). Suppose now that we switch systems of beliefs—somehow, you come to have my set of beliefs and I come to have yours. Given that coherence is entirely a matter of relations among beliefs, your system will be as coherent in my mind as it was in yours, and vice-versa. And yet, our beliefs are now completely unjustified—there you are, reading, believing that you are swimming, and here I am, swimming, believing that I am reading. In other words, certain transformations that preserve coherence in a system of beliefs do not seem to preserve justification.

In reply, coherentists have argued that it is possible to give experience a role without sacrificing the idea that coherence is entirely a matter of relations among beliefs—one idea is to require that any minimally acceptable system of beliefs contain beliefs about the experiences that the subject is undergoing (see BonJour 1985 and Lehrer 1990). It is fair to say that there is no agreement regarding whether this move can solve the problem.

One position that can be traced back to some ideas in Wittgenstein’s On Certainty (Wittgenstein 1969)—and, perhaps, also to Ortega’s Ideas y Creencias (Ortega y Gasset 1940)—is that evidential chains have to terminate in beliefs that are not properly said to be either justified or unjustified. This position, which we shall call “Positism” (not to be confused with “positivism”), shares many features with Foundationalism: for instance, both positists and foundationalists agree that inferential chains have to be finite and non-circular. But, whereas the foundationalist thinks that the starting points of inferential chains are beliefs that are justified by something other than beliefs, the positist thinks that the starting points of inferential chains are beliefs that are not justified by anything—they are posits that we have to believe without justification. Despite this difference between the positist and the foundationalist, the positions are structurally similar enough that analogues of the questions posed to the foundationalist can be asked of the positist. [ 21 ]

First, then, which beliefs are such that they are not justified and yet are the starting points of every inferential chain—in other words, how do we identify which are the posits? One answer that can be gleamed from Wittgenstein’s On Certainty , which we will call “relativistic Positism”, is that this is a matter that is relative both to time and society, because what the posits are is determined by some function of the actual positing practices of the members of one’s society at a certain time. Thus, according to Wittgenstein, the proposition that no one has been to the moon was a posit for a certain long period of time—it was a proposition that no one felt the need to justify, and that was presupposed in many justificatory practices. For obvious reasons, though, that proposition can no longer appropriately function as a posit. Other epistemologists, “non-relativistic positists”, think that which beliefs are properly posited depends on some objective truth about which beliefs have to be presupposed in order to engage in the practice of justifying beliefs at all. One prime candidate for playing this role is the first-person belief that I am not being deceived by an evil demon into thinking that I am a normally embodied and situated human being (this is the view advocated by Wright 2004 that we already alluded to in section 3.2).

The second question, regarding how posits must be related to inferred beliefs in order to justify them, can receive answers that are completely analogous to the foundationalists’.

The third question, applied to positism, is the question why certain beliefs are properly posited. Relativistic positists answer that this is so because of a certain societal fact: because they are taken to be so by an appropriate sub-sector of a certain society at a certain time. Non-relativistic positists answer that a certain belief is properly taken as a posit just in case every justificatory act that we engage in presupposes that the belief in question is true.

One objection that positists of both sorts have to face is that they are transforming a doxastic necessity into an epistemic virtue—that is, they are concluding that certain beliefs can properly serve as the starting points of inferential chains because that is how in fact they are treated (relativistic Positism) or because otherwise it wouldn’t be possible to engage in inferential practices at all (non-relativistic Positism). The Pyrrhonian skeptic, of course, will reply that the mere fact that most members of a society accept a certain belief without justification, or even the fact that if we don’t do so then we cannot justify anything else, doesn’t mean that it should be accepted without justification.

Perhaps the most interesting recent development in relation to Pyrrhonian Skepticism is that more and more epistemologists are arguing that the proper way to reply to Agrippa’s trilemma is to combine some of the positions that, for ease of exposition, we have presented as mutually exclusive. Thus, for example, many contemporary epistemologists put forward theories that contain elements of both Foundationalism and Coherentism (see, for instance, Haack 1993). It is a testament to the endurance of Pyrrhonian Skepticism that philosophers continue in this way to grapple with it.

• Aikin, Scott F., 2011, Epistemology and the Regress Problem , New York: Routledge.
• Aikin, Scott F. and Jeanne Peijnenburg (eds.), 2014, The Regress Problem: Metatheory, Development, and Criticism , special volume of Metaphilosophy , 45(2): 139–324.
• Anderson, Charity and John Hawthorne, 2019a, “Knowledge, Practical Adequacy, and Stakes”, in Oxford Studies in Epistemology (Volume 6), Tamar Szabó Gendler and John Hawthorne (eds.), Oxford: Oxford University Press, 234–257. doi:10.1093/oso/9780198833314.003.0010
• –––, 2019b, “Pragmatic Encroachment and Closure”, in Pragmatic Encroachment in Epistemology , Brian Kim and Matthew McGrath (eds.), (Routledge Studies in Epistemology), New York: Routledge.
• Audi, Robert, 1988, Belief, Justification and Knowledge , Belmont, CA: Wadsworth.
• BonJour, Laurence, 1978, “Can Empirical Knowledge Have a Foundation?”, American Philosophical Quarterly , 15(1): 1–13.
• BonJour, Laurence, 1985, The Structure of Empirical Knowledge , Cambridge, MA: Harvard University Press.
• Chisholm, Roderick M., 1966, Theory of Knowledge , Englewood Cliffs, NJ: Prentice Hall; first edition, 1966; second edition, 1977; third edition, 1989.
• –––, 1973, The Problem of the Criterion , Milwaukee: Marquette University Press.
• Cohen, Stewart, 1987, “Knowledge, Context, and Social Standards”, Synthese , 73(1): 3–26. doi:10.1007/BF00485440
• –––, 1988, “How to Be a Fallibilist”, Philosophical Perspectives , 2: 91–123. doi:10.2307/2214070
• –––, 1999, “Contextualism, Skepticism, and the Structure of Reasons”, Philosophical Perspectives , 13: 57–89. doi:10.1111/0029-4624.33.s13.3
• –––, 2000, “Contextualism and Skepticism”, Philosophical Issues , 10: 94–107. doi:10.1111/j.1758-2237.2000.tb00013.x
• –––, 2005, “Knowledge, Speaker and Subject”, Philosophical Quarterly , 55(219): 199–212.
• –––, 2010, “Bootstrapping, Defeasible Reasoning, and A Priori Justification”, Philosophical Perspectives , 24: 141–159. doi:10.1111/j.1520-8583.2010.00188.x
• –––, 2014a, “Contextualism Defended”, Steup, Turri, Sosa 2014: 69–75.
• –––, 2014b, “Contextualism Defended Some More”, in Steup, Turri, Sosa 2014: 79–83.
• Coliva, Annalisa, 2015, Extended Rationality: A Hinge Epistemology , London: Palgrave Macmillian. doi:10.1057/9781137501899
• Comesaña, Juan, 2005a, “Pyrrhonian Problematic, The”, in Encyclopedia of Philosophy , second edition, Donald M. Borchert (ed.), Macmillian.
• –––, 2005b, “Unsafe Knowledge”, Synthese , 146(3): 395–404. doi:10.1007/s11229-004-6213-7
• –––, 2007, “Knowledge and Subjunctive Conditionals”, Philosophy Compass , 2(6): 781–791. doi:10.1111/j.1747-9991.2007.00076.x
• –––, 2013, “Epistemic Pragmatism: An Argument Against Moderation”, Res Philosophica , 90(2): 237–260. doi:10.11612/resphil.2013.90.2.9
• –––, 2014a, “There is no Immediate Justification”, in Steup, Turri, and Sosa 2014: 222–234.
• –––, 2014b, “Reply to Pryor”, in Steup, Turri, and Sosa 2014: 239–243.
• –––, 2017, “On Sharon and Spectre’s Argument against Closure”, Philosophical Studies , 174(4): 1039–1046. doi:10.1007/s11098-016-0722-3
• –––, forthcoming, Being Rational and Being Right , Oxford: Oxford University Press.
• Conee, Earl, 2014a, “Contextualism Contested”, in Steup, Turri, and Sosa 2014: 60–68.
• Conee, Earl, 2014b, “Contextualism Contested Some More”, in Steup, Turri, and Sosa 2014: 75–78.
• David, Marian and Ted A. Warfield, 2008, “Knowledge-Closure and Skepticism”, in Epistemology: New Essays , Quentin Smith (ed.), Oxford: Oxford University Press, 137–187.
• DeRose, Keith, 1992, “Contextualism and Knowledge Attributions”, Philosophy and Phenomenological Research , 52(4): 913–929. doi:10.2307/2107917
• –––, 1995, “Solving the Skeptical Problem”, The Philosophical Review , 104(1): 1–52. doi:10.2307/2186011
• –––, 2002, “Assertion, Knowledge, and Context”, The Philosophical Review , 111(2): 167–203. doi:10.2307/3182618
• –––, 2004, “The Problem with Subject-Sensitive Invariantism”, Philosophy and Phenomenological Research , 68(2): 346–350. doi:10.1111/j.1933-1592.2004.tb00346.x
• –––, 2005, “The Ordinary Language Basis for Contextualism, and the New Invariantism”, The Philosophical Quarterly , 55(219): 172–198. doi:10.1111/j.0031-8094.2005.00394.x
• DeRose, Keith and Ted A. Warfield (eds.), 1999, Skepticism: A Contemporary Reader , New York and Oxford: Oxford University Press.
• Descartes, René, 1641, Meditations on First Philosophy , Leiden; translated in Philosophical Works of Descartes (Volume 1), Elizabeth S. Haldane and G.R.T. Ross (eds./trans.), New York: Dover Publications, 1931.
• Dretske, Fred I., 1970, “Epistemic Operators”, The Journal of Philosophy , 67(24): 1007–1023. doi:10.2307/2024710
• Fantl, Jeremy and Matthew McGrath, 2002, “Evidence, Pragmatics, and Justification”, The Philosophical Review , 111(1): 67–94. doi:10.2307/3182570
• –––, 2007, “On Pragmatic Encroachment in Epistemology”, Philosophy and Phenomenological Research , 75(3): 558–589. doi:10.1111/j.1933-1592.2007.00093.x
• –––, 2009, Knowledge in an Uncertain World , New York and Oxford: Oxford University Press. doi:10.1093/acprof:oso/9780199550623.001.0001
• Feldman, Richard and Earl Conee, 1985, “Evidentialism”, Philosophical Studies , 48(1): 15–34. doi:10.1007/BF00372404
• Firth, Roderick, 1978, “Are Epistemic Concepts Reducible to Ethical Concepts?”, in Values and Morals: Essays in Honor of William Frankena, Charles Stevenson, and Richard Brandt , Alvin I. Goldman and Jaegwon Kim (eds.), Dordrecht: Springer Netherlands, 215–229. doi:10.1007/978-94-015-7634-5_12
• Foley, Richard, 1993, Working Without a Net: A Study of Egocentric Epistemology , New York and Oxford: Oxford University Press.
• Frances, Bryan, 2005, Scepticism Comes Alive , New York: Oxford University Press. doi:10.1093/0199282137.001.0001
• Friedman, Jane, 2013, “Suspended Judgment”, Philosophical Studies , 162(2): 165–181. doi:10.1007/s11098-011-9753-y
• –––, 2017, “Why Suspend Judging?”, Noûs , 51(2): 302–326. doi:10.1111/nous.12137
• Goldman, Alvin I., 1979, “What Is Justified Belief?”, in Justification and Knowledge: New Studies in Epistemology , George Sotiros Pappas (ed.), Dordrecht: Springer Netherlands, 1–23. doi:10.1007/978-94-009-9493-5_1
• Haack, Susan, 1993, Evidence and Inquiry: Towards Reconstruction in Epistemology , Oxford: Blackwell.
• Hawthorne, John, 2003, Knowledge and Lotteries , Oxford: Oxford University Press. doi:10.1093/0199269556.001.0001
• –––, 2014, “The Case for Closure”, in Steup, Turri, and Sosa 2014: 40–56.
• Hobbes, Thomas, 1651, Leviathan , London.
• Huemer, Michael, 2001, “The Problem Of Defeasible Justification”, Erkenntnis , 54(3): 375–397. doi:10.1023/A:1010718330593
• Klein, Peter D., 1981, Certainty: A Refutation of Scepticism , Minneapolis, MN: University of Minnesota Press.
• –––, 1995, “Skepticism and Closure: Why the Evil Genius Argument Fails”, Philosophical Topics , 23(1): 213–236.
• –––, 1999, “Human Knowledge and the Infinite Regress of Reasons”, Philosophical Perspectives , 13: 297–325. doi:10.1111/0029-4624.33.s13.14
• –––, 2000, “Contextualism and the Real Nature of Academic Skepticism”, Philosophical Issues , 10: 108–116. doi:10.1111/j.1758-2237.2000.tb00014.x
• –––, 2007, “Human Knowledge and the Infinite Progress of Reasoning”, Philosophical Studies , 134(1): 1–17. doi:10.1007/s11098-006-9012-9
• Lehrer, Keith, 1990, Theory of Knowledge , Boulder, CO: Westview Press.
• Lewis, David, 1996, “Elusive Knowledge”, Australasian Journal of Philosophy , 74(4): 549–567. doi:10.1080/00048409612347521
• Luper-Foy, Steven (ed.), 1987, The Possibility of Knowledge: Nozick and His Critics , Totowa, NJ: Rowman & Littlefield.
• McGrath, Matthew, 2004, “Review of Knowledge and Lotteries , by John Hawthorne”, Notre Dame Philosophical Reviews , 2004.08.06. URL = < https://ndpr.nd.edu/news/knowledge-and-lotteries/ >
• Moore, G.E., 1939 [1993], “Proof of an External World”, Proceedings of the British Academy , 25(5): 273–300; reprinted in his Philosophical Papers , New York, NY: Macmillan, 1959; reprinted in G. E. Moore: Selected Writings , T. Baldwin (ed.), Routledge, London, 1993: 147–70.
• Nozick, Robert, 1981, Philosophical Explanations , Cambridge, MA: Harvard University Press.
• Ortega y Gasset, José, 1940, Ideas y Creencias , Buenos Aires: Espasa Calpe.
• Peijnenburg, Jeanne and Sylvia Wenmackers (eds.), 2014, “Infinite Regress in Decision Theory, Philosophy of Science, and Formal Epistemology”, special issue of Synthese , 191(4): 627–723.
• Pryor, James, 2000, “The Skeptic and the Dogmatist”, Noûs , 34(4): 517–549. doi:10.1111/0029-4624.00277
• –––, 2004, “What’s Wrong with Moore’s Argument?”, Philosophical Issues , 14: 349–378. doi:10.1111/j.1533-6077.2004.00034.x
• –––, 2014a, “There Is Immediate Justification”, in Steup, Turri, and Sosa 2014: 202–221.
• –––, 2014b, “Reply to Comesaña”, in Steup, Turri, and Sosa 2014: 235–238.
• Quine, W.V. and J. S. Ullian, 1970 [1978], The Web of Belief , New York: Random House; second edition, 1978.
• Radford, Colin, 1966, “Knowledge—By Examples”, Analysis , 27(1): 1–11. doi:10.2307/3326979
• Rinard, Susanna, 2018, “Reasoning One’s Way Out of Skepticism”, in The Mystery of Skepticism: New Explorations , Kevin McCain and Ted Poston (eds.), Leiden: Brill. doi:10.1163/9789004393530_015
• Sharon, Assaf and Levi Spectre, 2017, “Evidence and the Openness of Knowledge”, Philosophical Studies , 174(4): 1001–1037. doi:10.1007/s11098-016-0723-2
• Sosa, Ernest, 2002, “Tracking, Competence, and Knowledge”, in The Oxford Handbook of Epistemology , Paul K. Moser (ed.), Oxford: Oxford University Press.
• Stanley, Jason, 2005, Knowledge and Practical Interests , Oxford: Oxford University Press. doi:10.1093/0199288038.001.0001
• Steup, Matthias, John Turri, and Ernest Sosa (eds.), 2014, Contemporary Debates in Epistemology (Contemporary Debates in Philosophy 14), second edition, Chichester: Wiley Blackwell.
• Stine, G. C., 1976, “Skepticism, Relevant Alternatives, and Deductive Closure”, Philosophical Studies , 29(4): 249–261. doi:10.1007/BF00411885
• Turri, John and Peter D. Klein (eds.), 2014, Ad Infinitum: New Essays on Epistemological Infinitism , New York: Routledge.
• Van Cleve, James, 2005, “Why Coherence is Not Enough: A Defense of Moderate Foundationalism”, in Steup, Turri, and Sosa 2014: 255–266.
• Vogel, Jonathan, 1987, “Tracking, Closure and Inductive Knowledge”, in Luper-Foy 1987: 197–215.
• –––, 1990, “Cartesian Skepticism and Inference to the Best Explanation”, Journal of Philosophy , 87(11): 658–666.
• –––, 2014a, “E & \(\sim H*\)”, in Scepticism and Perceptual Justification , Dylan Dodd and Elia Zardini (eds.), Oxford: Oxford University Press, 87–107. doi:10.1093/acprof:oso/9780199658343.003.0005
• –––, 2014b, “The Refutation of Skepticism”, in Steup, Turri, and Sosa 2014: 108–120.
• Wedgwood, Ralph, 2013, “A Priori Bootstrapping”, in The A Priori in Philosophy , Albert Casullo and Joshua C. Thurow (eds), Oxford: Oxford University Press.
• Williamson, Timothy, 2000, Knowledge and Its Limits , Oxford: Oxford University Press.
• Wilson, Jessica, 2012, “The Regress Argument against Cartesian Skepticism”, Analysis , 72(4): 668–673. doi:10.1093/analys/ans117
• Wittgenstein, Ludwig, 1969, On Certainty , Denis Paul and G. E. M. Anscombe (trans.), Oxford: Basil Blackwell.
• Wright, Crispin, 2004, “Warrant for Nothing (and Foundations for Free)?”, Aristotelian Society Supplementary Volume , 78: 167–212. doi:10.1111/j.0309-7013.2004.00121.x

How to cite this entry . Preview the PDF version of this entry at the Friends of the SEP Society . Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO). Enhanced bibliography for this entry at PhilPapers , with links to its database.

• Links to papers on Skepticism , in the Epistemology Research Guide, maintained by Keith Korcz (U. Louisiana/Fayetteville)

Descartes, René: epistemology | epistemic closure | justification, epistemic: coherentist theories of | justification, epistemic: foundationalist theories of | justification, epistemic: internalist vs. externalist conceptions of | perception: the disjunctive theory of | skepticism: ancient | transmission of justification and warrant

Acknowledgments

Thanks to an anonymous referee for helpful suggestions.

Copyright © 2019 by Juan Comesaña < juan . comesana @ rutgers . edu > Peter Klein

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6 Theories of Justification

Richard Fumerton is F. Wendell Miller Professor of Philosophy at the University of Iowa.

• Published: 02 September 2009
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The task of explaining and evaluating theories of justification is daunting. There are not only a host of different theories of justification, there are also radical differences among epistemologists concerning how they understand what it is to offer such a theory. This article offers an overview of several prominent positions on the nature of justification. It begins by isolating epistemic justification from nonepistemic justification. It also distinguishes between “having justification for a belief” and “having a justified belief,” arguing that the former is conceptually more fundamental. It then addresses the possibility that justification is a normative matter, suggesting that this possibility has little to offer a concept of epistemic justification. It also critically examines more specific attempts to capture the structure and content of epistemic justification. These include traditional foundationalism and variants thereof, externalist versions of foundationalism; contextualism; coherentism; and “mixed” theories which combine aspects of coherentism and foundationalism.

The concept of justification may be the most fundamental in epistemology. On what became the dominant view in the twentieth century, knowledge is to be understood, at least in part, through our understanding of justification. Part of the answer many offer to Plato's question in the Theaetetus , “What must be added to true belief in order to get knowledge?” is justification. Furthermore, on many accounts of knowledge and justification, it is tempting to conclude that the only responsibility we are competent to carry out qua philosophers is to conform our beliefs to what is justified. Whether or not the world cooperates so as to turn those justified beliefs into knowledge is out of our hands.

The task of explaining and evaluating theories of justification, however, is daunting. There are not only a host of different theories of justification, there are also radical differences among epistemologists concerning how they understand what it is to offer such a theory. Some epistemologists are trying to identify the properties that constitute having justification while others are trying to identify properties upon which justification supervenes. Some philosophers take the product of their analyses to be analytic truths; others claim to be engaged in some sort of empirical investigation. In addition to the fact that there are these meta‐philosophical and methodological controversies lurking in the background, there are serious questions as to whether epistemologists have even agreed on the target of their analyses. Let us begin with this last question.

Epistemic vs. Nonepistemic Justification

The first distinction an epistemologist should emphasize before putting forth a theory of justification is that between epistemic justification and other sorts of justification. If I ask whether S's belief is justified or rational, I might be concerned, for example, with prudential justification. It seems to be a fact that a patient's believing that she will get well often increases the chances of her recovery (even if the resulting probability remains very low). In such a situation there is surely some sense in which the patient would be justified in having (or at least trying to get) the optimistic belief. But even if we allow that there is a sense in which the belief is justified or rational, we don't want to allow that it is epistemically justified or rational. Or consider the person who is becoming paralyzed by fear of death. If believing that there is an afterlife will alleviate that fear and allow the person to live a normal life, then there is again a sense in which it would be perfectly reasonable for that person to try to bring about the belief that has this effect. Prudential reasons for believing (if they exist) have something to do with the efficacy with which believing will or might achieve certain goals or ends.

There may be other nonepistemic reasons for believing or failing to believe a given proposition. It is not wildly implausible to suppose, for example, that a husband has a special moral obligation, and with it a moral reason , to believe that his wife is faithful even in the face of rather powerful epistemic reasons for believing otherwise. One could even imagine a kind of “ 1984 culture” in which one has legal obligations, and legal reasons, to have certain beliefs that are, nevertheless, epistemically irrational.

Can we find a way of characterizing epistemic justification that is relatively neutral with respect to opposing analyses of the concept? As a first stab we might suggest that whatever else epistemic justification for believing some proposition is, it must make probable the truth of the proposition believed. 1 The patient with prudential reasons for believing in a recovery was more likely to get that recovery as a result of her beliefs, but the prudential reasons possessed did not increase the probability of the proposition believed—it was the belief for which the person had prudential reasons that resulted in the increased probability. Epistemic reasons make likely the truth of what is supported by those reasons, and, although it is controversial, it is tempting to suggest that the relation of making likely is not to be understood in causal terms.

Our preliminary characterization of justification as that which makes probable the truth of a proposition may not in the end be all that neutral. As we shall see in a moment, there are those who stress an alleged normative feature of epistemic justification that may call into question the conceptual primacy of probability as a key to distinguishing epistemic reasons from other sorts of reasons. Furthermore, as we shall also see, if one understands the relation of making probable in terms of a frequency conception of probability, one will inevitably beg the question with respect to certain internalist/externalist debates over the nature of justification.

Having Justification for a Belief and Having a Justified Belief

Another preliminary, but important, distinction to stress is that between having justification for a belief and having a justified belief. There seems to be a perfectly clear sense in which there may be enormously strong epistemic reasons for me to believe a given proposition even though I don't end up believing it. In such a situation we can say that there was justification for me to believe the proposition even though I didn't, of course, have a justified belief (or a belief at all) in the relevant proposition. 2 It is tempting to suppose that we can employ the concept of having justification for believing P to define what it is for a person to justifiably believe P. Specifically, one might suggest that a person justifiably believes P when that person believes P and does so based on justification that the person possesses. The analysis of the basing relation is a matter of much controversy. One might hope to analyze it in causal terms. If there is justification J for S to believe P, then S believes P justifiably just in case S's belief is caused by the fact that there is justification for him to believe P. When one presents causal analyses of any concept, however, one should immediately be on guard against counterexamples that rely on “deviant” causal chains. If I possess justification J for believing P, and that causes the hypnotist at the party to hypnotize me into believing P when I hear a doorbell ring, it is not at all clear that I have based the resulting belief on the justification I possess. There may be some relatively straightforward way to revise a causal account of basing to take care of such problems (by, for example, insisting that the causal connection has to be in some sense direct) but we won't explore this issue further here.

If the distinction between possessing justification and having a justified belief is legitimate, which if either of these concepts is more fundamental? If the suggestion made above were plausible, then clearly having justification would be conceptually more fundamental than having a justified belief. We are defining the latter in terms of the former. Furthermore, if we understand the basing relation in causal terms, we should beware of philosophers speculating about which beliefs are or are not justified. One needs empirical evidence to support a causal hypothesis, and it strikes me that philosophers are rarely in possession of the empirical evidence they would need in order to support a psychological claim about what is or is not causing a given belief. Although epistemologists have often supposed that they are trying to determine which beliefs are justified and which are not, I would suggest that if they are to restrict themselves to questions they are competent to answer, qua philosophers, they ought to concern themselves only with the question of whether there is justification for us to believe this or that proposition. Depending on one's analysis of justification, this question may itself end up being an empirical question that philosophers are not particularly competent to address, but this is an issue to which we shall return.

Justification and Normativity

A surprising number of philosophers, with radically different theories of justification, seem to agree that justification is a normative concept. Unfortunately, it is not at all clear what philosophers have in mind by characterizing a concept as normative. We might begin by suggesting that normative terms are those whose meaning can be explicated using paradigm normative expressions, and we might simply list that which is paradigmatically normative. The list might be long or short depending on whether or not we think that all normative expressions can be defined in terms of a relatively few fundamental normative notions. So one might include among the paradigmatically normative such terms as “good,” “ought,” should,” “right,” “permissible,” obligatory,” and their opposites.

If we proceed in this fashion it seems undeniable that the concept of epistemic justification looks suspiciously like a normative concept. As Plantinga ( 1992 ) has effectively reminded us, the etymology of the word “justification” certainly suggests that we are dealing with a value term. And epistemologists often seem quite comfortable interchanging questions about whether or not evidence E justifies one in believing P with questions about whether or not one should believe P on the basis of E. In what is often taken to be an early statement of a justified true belief account of knowledge, Ayer ( 1956 ) described knowledge as true conviction where one has the right to be sure. So again the idea that the concept of justification is normative is at least prima facie plausible. But we must proceed cautiously. We have already seen that we must distinguish epistemic reasons from other sorts of reasons. If we can translate talk about justified belief into talk about what we ought to believe, these same considerations suggest that we must distinguish different senses of “ought.” In the prudential sense of “ought,” perhaps the patient ought to believe she will get better. In the moral sense of “ought,” perhaps the husband ought to believe in his spouse's innocence. But the epistemologist is concerned with what one epistemically ought to believe, and we still need to be convinced that there is some interesting sense in which all of these different “ought” 's express normative concepts.

If we take as our paradigm of a normative “ought” the moral “ought,” then I suppose the question of whether the epistemic “ought” expresses a normative concept reduces to the question of whether there are interesting connections between it and the moral “ought.” The problem now is that moral philosophers have radically different views about what makes moral “ought” judgments normative. Some try to distinguish the normative from the nonnormative by contrasting prescriptive judgements with descriptive judgments. But if this is supposed to be the normative/nonnormative distinction, it is far from clear that the epistemologist should accept the claim that justification is a normative concept. I suspect that most epistemologists take a belief 's being justified to be a fact that admits of description just as straightforwardly as a belief 's having a certain causal history. (Indeed, on some theories of justification a belief 's being justified just is a matter of its causal history.)

Richard Foley ( 1987 ) has suggested that we might understand epistemic justification in terms of what one ought to believe, and he goes on to understand the difference between the epistemic “ought” and other “oughts” 's as differences between species of a common genera. Crudely put, Foley's idea is that normative judgments all assess the efficacy of achieving goals or ends. There are different kinds of normative judgments concerning what we ought to do and what we ought to believe because there are different goals or ends that we are concerned to emphasize. Thus when we are talking about morally justified action, the relevant goal might be something like creating good and avoiding evil. When we are concerned with what prudence dictates, the relevant goals or ends change, perhaps to include everything that is desired intrinsically, for example. What one legally ought to do is a function of the extent to which an action satisfies the goal of following the law. To fit the epistemic “ought” into this framework (and thus classify usefully the kind of normativity that epistemic judgments have) all one needs to do is specify the distinctive goals or ends that define what one epistemically ought to believe. And the obvious candidates are the dual goals of believing what is true and avoiding belief in what is false.

Suggestive as this account might seem, it faces enormous difficulties. It must be immediately qualified to accommodate certain obvious counterexamples. Let's return to our paradigm of a nonepistemic reason, the reason the patient had for believing that she would get well. By forming the relevant belief, the patient might produce for herself a long life which she could devote to scientific and philosophical investigation, investigation that results in an enormous number of true beliefs. Despite accomplishing the goal of believing what is true, our patient (by hypothesis) had no epistemic reason for believing that she would get well. The obvious solution to this problem (one Foley suggests) is to restrict the relevant epistemic goal to that of now believing what is true and now avoiding belief in what is false. But such a revision doesn't really address the problem. Suppose there is an all powerful being who will immediately cause me to believe massive falsehood now unless I accept the epistemically irrational conclusion that there are mermaids. It would seem that to accomplish the goal of believing what is true and avoiding belief in what is false now I must again adopt an epistemically irrational belief.

In desperation one might try restricting the relevant epistemic goal to that of believing what is true now with respect to a given proposition. But now we are in danger of collapsing the distinction between true belief and justified belief. Trivially, the only way to accomplish the goal of believing what is true with respect to P is to believe P when P is true. The problem is that one really wants to identify the content of the epistemic “ought” with what one is justified in believing will accomplish the goal of now believing what is true with respect to a given proposition. But with this revision our “goal” oriented account of epistemic justification becomes pathetically circular. 3

There are, of course, other ways to try to understand the alleged normative character of epistemic justification, but I'm not sure any are illuminating. One might suppose that when one characterizes a belief as justified one is indicating that it is not an appropriate subject for criticism. When one says of a belief that it is unjustified, one is criticizing the belief. For the view to gain even initial credibility, it would be important to distinguish the criticism of a belief from the criticism of the subject who holds the belief. It is simply false that we would always criticize a person for holding a belief we judge to be epistemically irrational. We might, for example, suppose that the person is just too stupid to be able to evaluate properly the relevant evidence and we might, as a result, seldom criticize him for the many wildly irrational beliefs he holds. But even if one makes clear that in characterizing a belief as unjustified one is criticizing the belief not the believer, I'm not sure that one can successfully argue that a person would be guilty of contradiction if, in the grips of some rebellious “anti‐reason” movement, that person criticizes beliefs that conform to the dictates of epistemic rationality.

Once one clearly distinguishes the epistemic “ought” from others it is not in the end clear that one gets much understanding of the concept of justification from the suggestion that epistemic judgements are in some sense normative. 4

Foundationalism

It is tempting to think that one can leave the question of how to understand epistemic justification aside and distinguish different theories of justification in terms of how they understand the structure of epistemic justification. Perhaps the most famous theory of epistemic justification is foundationalism —the very term for the view employs a structural metaphor. But as we shall see, foundationalism is probably best understood not just as a view about the structure of justification. Properly understood, different versions of foundationalism also give an account of the content of epistemic judgements.

Traditional versions of foundationalism have fallen on hard times, but given the present popularity of its externalist cousins, it is still probably the received view in epistemology. Put crudely, the foundationalist believes that all justified beliefs rest ultimately on a foundation of noninferentially justified beliefs. One gets radically different versions of foundationalism depending on how the foundationalist understands noninferential justification.

A little reflection suggests that the vast majority of the propositions for which we have justification have that status only because we justifiably believe other different propositions. So, for example, I justifiably believe that Hitler killed himself, but only because I justifiably believe (among other things) that various generally reliable historical texts describe the event. Foundationalists want to contrast my inferential justification for this belief about Hitler with a kind of justification that is not constituted , in whole or in part, by the having of other justified beliefs. But why should we suppose that there is a kind of justification that is in this way different from inferential justification?

The Regress Arguments for Foundationalism

Suppose I tell you as you approach your fiftieth birthday that you will shortly go insane. I offer as my evidence that you have a genetic defect that, like a time bomb, goes off at the age of 50. Naturally alarmed, you ask me what reason I have for concluding that you have the gene. I respond that it is just a hunch on my part. As soon as you discover that I have no epistemic justification at all for believing that you have the gene, you will immediately conclude that my bizarre conclusion about your impending insanity is wildly irrational. Generalizing from examples like this, one might suggest the following principle:

To be justified in believing P on the basis of E one must be justified in believing E

Now consider another example. Suppose I claim to be justified in believing that Fred will die shortly and offer as my justification that a certain line across his palm (the infamous “lifeline”) is short. Rightly skeptical you wonder this time what reason I have for believing that palm lines have anything whatsoever to do with length of life. As soon as you become satisfied that I have no justification for supposing that there is any kind of probabilistic connection between the character of this line and Fred's life, you will again reject my claim to have a justified belief about Fred's impending demise. 5 That suggests that we might expand our Principle of Inferential Justification (PIJ) to include a second clause:

To have justification for believing P on the basis of E one must not only have (1) justification for believing E, but (2) justification for believing that E makes probable P.

The Epistemic Regress Argument

With PIJ one can present a relatively straightforward epistemic regress argument for foundationalism. If all justification were inferential then for someone S to have justification for believing some proposition P, S must be in a position to legitimately infer it from some other proposition E1. But E1 could justify S in believing P only if S were justified in believing E1, and if all justification were inferential, the only way for S to be justified in believing E1 would be to infer it from some other proposition E2 justifiably believed, a proposition which in turn would have to be inferred from some other proposition E3, which is justifiably believed, and so on, ad infinitum. But finite beings cannot complete an infinitely long chain of reasoning and so, if all justification were inferential, no‐one would be justified in believing anything at all to any extent whatsoever. This most radical of all skepticisms is absurd (it entails that one couldn't even be justified in believing it) and so there must be a kind of justification that is not inferential, that is, there must be noninferentially justified beliefs which terminate regresses of justification.

If we accept the more controversial second clause of PIJ, the looming regresses proliferate. Not only must S above be justified in believing E1, S must also be justified in believing that E1 makes probable P, a proposition that would have to be inferred (if there are no foundations) from some other proposition F1, which would have to be inferred from F2, and so on ad infinitum. But S would also need to be justified in believing that F1 does in fact make likely that E1 makes likely P, a proposition he would need to infer from some other proposition G1, which he would need to infer from some other proposition G2… . And he would need to infer that G1 does indeed make likely that F1 makes likely that E1 makes likely P… . Without noninferential justification, it would seem that we would need to complete an infinite number of infinitely long chains of reasoning in order to be justified in believing anything!

Peter Klein ( 1999 ) has recently defended a view he calls infinitism . The infinitist refuses to accept the existence of noninferential justification, acknowledges that with the availability of only inferential justification, justified belief would require us to be able to come up with infinitely many arguments for infinitely many premises, but argues that finite beings might very well have the capacity to do just that. There is nothing absurd in the supposition that people have an infinite number of justified beliefs (most of which are not, of course, conscious at any given time). You believe justifiably that 2>1, that 3>1, that 4>1, and so on, ad infinitum. While you cannot, of course, complete an infinitely long chain of reasoning, you might be such that you could offer an argument for every proposition you believe. And there is nothing absurd about the suggestion that your ability to do just that is necessary for each of your beliefs being justified.

There seems to be something very odd about the idea that I need arguments to support some of my beliefs, for example, the belief that I'm in pain now, or the belief that I exist now. But even if the availability of an infinite number of dispositional beliefs weakens the foundationalists' claim that without noninferential justification we inevitably face skepticism, it's not clear that the infinitist has a rejoinder to a second regress argument for foundationalism.

The Conceptual Regress Argument

The epistemic regress argument discussed above relies on the unacceptability of a vicious epistemic regress. But one might also argue, more fundamentally, that without a concept of noninferential justification, one faces a vicious conceptual regress. What precisely is our understanding of inferential justification? What makes PIJ true (with or without its controversial second clause). It is at least tempting to answer that PIJ is analytic (true by definition). Part of what it means to claim that someone has inferential justification for believing some proposition P is that his justification consists in his ability to infer P from some other proposition E1 that is justifiably believed. But if anything like this is a plausible analysis of the concept of inferential justification, we face a potentially vicious conceptual regress. Our understanding of inferential justification presupposes an understanding of justification. We need to introduce a concept of noninferential justification in terms of which we can then ultimately define inferential justification.

Consider an analogy. Suppose a philosopher introduces the notion of instrumental goodness (something's being good as a means). That philosopher offers the following crude analysis of what it is for something to be instrumentally good: X is instrumentally good when X leads to something Y, which is good. Even if we were to accept this analysis of instrumental goodness, it is clear that we haven't yet located the conceptual source of goodness. Our analysis of instrumental goodness presupposes an understanding of what it is for something to be good and ultimately presupposes an understanding of what it is for something to be intrinsically good. The conceptual regress argument for foundationalism puts forth the thesis that inferential justification stands to noninferential justification as instrumental goodness stands to intrinsic goodness.

Noninferential Justification

If there is a conceptual regress argument for foundationalism, then one hasn't completed one's foundationalist account of epistemic justification until one gives an account of noninferential justification, an account that itself employs no epistemic concepts. Those who continue to insist that epistemic justification is a normative concept, who reject naturalistic accounts of value, and who further claim that fundamental normative concepts cannot be defined, might claim that an account of noninferential justification consists in an identification of the properties of a belief or a believer upon which noninferential justification supervenes (Goldman 1979 ). The term “supervenience” is a piece of philosophical jargon upon which many these days rely. To say that Y supervenes upon X is usually just to claim that there is some sort of necessary connection between X and Y where one can distinguish as many species of supervenience as one can distinguish kinds of necessary connections. In what follows, I'm going to discuss different accounts of noninferential justification in terms of the conditions with which the proponent of the view identifies having noninferential justification. If one is nervous about identity claims one can translate the views into the language of supervenience.

Noninferential Justification as Infallible Belief

Descartes may be the most well‐known foundationalist. Although he almost never talked about justification (his concern was with knowledge), it seems clear that he embraced the idea that there is a way of knowing that does not rely on what we have called inferential justification. On the most natural interpretation of his views, Descartes identified foundational knowledge with infallible belief. Famously, Descartes found his “first” truth in knowledge of his own existence. What distinguished Descartes's belief about his own existence from other beliefs is that the mere fact that he believed that he existed entailed that he did. Shall we understand noninferential justification in Cartesian terms? Shall we say that S's belief that P is noninferentially justified at t when S's believing P at t entails that P is true?

There are relatively few Cartesian foundationalists around these days. The view is plagued with difficulties. As Lehrer ( 1974 ) and others have pointed out, it is far from clear that this concept of infallible belief has much relevance to our fundamental understanding of noninferential justification. Consider just one technical problem. Every necessary truth is trivially entailed by all propositions (P entails Q when it is impossible for P to be true while Q is false, but if it is impossible for Q to be false then it is entailed by everything). So given the above way of understanding infallible belief, all belief in necessary truth would have noninferential justification. But this just seems wrong. If I whimsically believe some proposition whose necessity is far too complicated for me to grasp, it hardly seems plausible to maintain that the belief would have noninferential justification.

Even if we can find a way of solving the above problem, most contemporary epistemologists are convinced that foundational justification restricted to what can be infallibly believed allows far too insubstantial a foundation to support the complex edifice of what we take ourselves to be justified in believing. There may be a few contingent propositions that are trivially entailed by the fact that they are believed—my belief that I exist, that I have beliefs, that I am conscious—but once we get past propositions whose very subject matter encompasses the fact that they are believed, it's hard to come up with uncontroversial examples of infallible beliefs. As Ayer ( 1956 , 19) argued, as long as the belief that P is one state of affairs and P's being the case is an entirely different state of affairs, it's hard to see how it can be impossible for the former to occur without the latter.

Infallible Justification

Rather than try to identify noninferential justification with some intrinsic feature of a belief that renders the belief infallible, one might instead look for a kind of justification that can accompany a belief and eliminate the possibility of error. Let us say that S's belief that P is infallibly justified at t when S's justification for believing P at t contains as a constituent the very truth‐maker for P. But how can the justification for a belief be identified with a state of affairs that includes as a constituent something that makes true the belief?

Some traditional foundationalists have held that beliefs about experiences are justified by the very experiences that are the subject matter of the beliefs. Thus, for example, it might seem initially plausible to suppose that when I am in pain, it is the pain itself that justifies me in believing that I am in pain. On such a view, the noninferential justification I have for believing that I'm in pain—the experience of pain—trivially guarantees the truth of what I believe. But such a view clearly cries out for some further account of what distinguishes the experience of pain from, say, Caesar's assassination. The above foundationalist wants to claim that while the fact that I'm in pain can justify me in believing that I'm in pain, the fact that Caesar was assassinated cannot justify my belief that Caesar was assassinated. But what is the relevant difference between the two facts that makes it implausible to claim that one is a noninferential justifier, while the other is not? It won't do to call attention to the fact that the pain is an experience of mine. My body is undergoing all sorts of changes right now, the vast majority of which don't justify me in believing that they are occurring. So we still need a principled account of what distinguishes those states of mine that can justify beliefs about them from those states of mine that cannot.

It is tempting to suppose that the foundationalist is better off appealing to some special relation that I have to my pain that makes it unnecessary to look to other beliefs in order to justify my belief that I'm in pain. It's not my pain that justifies me in believing that I'm in pain. It is, rather, the fact that I have a kind of access to my pain that no‐one else has that makes my belief noninferentially justified (while others must rely on inference in order to discover that I'm in this state). The sort of access this foundationalist appeals to is not, of course, justified belief. We need an understanding of noninferential justification that does not rely on an understanding of justified belief. Bertrand Russell ( 1959 and 1984 ) contrasted acquaintance with properties and facts with propositional knowledge. Acquaintance is a sui generis relation that a subject bears to certain facts in virtue of which the subject gets a kind of justification for believing the propositions made true by those facts. A slightly more complicated version of the view maintains that one is noninferentially justified in believing a proposition P when one is directly acquainted with not only the fact that P but also with a relation of correspondence between the thought that P and the fact that P (where the correspondence between a thought and a fact is the essence of a thought's being true). Since acquaintance is a relation that requires the existence of its relata, there is a trivial sense in which one can't possess this sort of justification for believing a proposition while the proposition is false. 6

The acquaintance theory might have one interesting advantage over alternative theories in that it has the potential to offer a unified account of noninferential justification. According to most traditional foundationalists, two of the best candidates for noninferentially justified beliefs are empirical beliefs about the current contents of one's mind and a priori beliefs about relatively straightforward necessary truths. On the acquaintance theory, both direct knowledge of necessary truths and direct knowledge of contingent truths about one's current consciousness would have the same source of justification—acquaintance with facts. The difference between the two kinds of knowledge is not so much a difference in the sources of the knowledge but in the contents of the knowledge. The objects of acquaintance in the case of direct knowledge of mental states are states of affairs whose occurrence is not eternal—the objects of acquaintance in the case of direct knowledge of necessary truths are eternal states of affairs.

Objections to Traditional Foundationalism

In one of the most influential arguments against foundationalism, Wilfrid Sellars ( 1963 , 131–132) argued that the idea of foundational justification as something's being “given” to one in consciousness (something's being an object of direct acquaintance) contains irreconcilable tensions. On the one hand, to ensure that something's being given does not involve any other beliefs, proponents of the view want noninferential justification to be untainted by the application of concepts. On the other hand, the whole point of foundationalism is to end a regress of justification, to give us secure foundational justification for the rest of what we justifiably infer from those foundations. But to make sense of inferences from our foundations, we must ensure that what is given to us in consciousness has a truth value . The kind of thing that has a truth value, however, involves the application of concepts. But to apply a concept is to make a judgment about class membership, and to make a judgment about class membership always involves relating the thing about which the judgment is made to other paradigm members of the class. These judgments of relevant similarity will minimally involve beliefs about the past and thus be inferential in character (assuming that we can have no “direct” access to facts about the past).

The above objection obviously relies on a host of controversial presuppositions. In order to deflect the force of the objection, a traditional acquaintance foundationalist will no doubt emphasize the following. Being directly acquainted with a fact is not, by itself, to have a justified belief in some proposition. It is only acquaintance with a fact conjoined with awareness of a thought's corresponding to a fact that constitutes having noninferential justification. There may well be all kinds of creatures who have acquaintance with facts but no justification for believing anything precisely because they lack the capacity to form thoughts. Secondly, the classical foundationalist will, or at least should, reject the suggestion that to apply a concept is to relate the thing to which one applies the concept to other entities that fall under the concept. Such a view simply invites a vicious regress of the sort that the foundationalist is trying so desperately to avoid. After all, my judgement that X is similar to Y itself involves applying the concept of similarity to the pair X/Y. In doing so am I comparing the pair X/Y to other things that are similar to each other? In fact, I can judge something to be pain without having any recollection whatsoever of any other experience that I have had.

The direct acquaintance theorist does presuppose the intelligibility of acquaintance with facts and in doing so presupposes the intelligibility of a world that has “structure” independently of any structure imposed by the mind. Certain radical versions of “antirealism” reject that commitment to a strong “correspondence” conception of truth and with it the intelligibility of a thought/world fit of which we can be directly aware. 7 While there is some plausibility to the claim that there are, in some sense, alternative conceptual frameworks that we can impose on the world, it is surely absurd to suppose that it is even in principle possible for a mind to force a structure on a literally unstructured world. There are indefinitely many ways to sort the books in a library and some are just as useful as others, but there would be no way to begin sorting books were books undifferentiated. If we couldn't take notice of differences in the world with which we are acquainted, it's not clear how we could “choose” conceptual frameworks with which to make sense of our experience.

Laurence BonJour ( 1985 ) raised another highly influential objection to all forms of classical foundationalism (an objection raised before he himself joined the ranks of the traditional foundationalists). The objection presupposed a strong form of what we might call access internalism. Put superficially, the access internalist argues that a feature of a belief or epistemic situation that makes a belief noninferentially justified must be a feature to which we have actual or potential access. Moreover, we must have access to the fact that the feature in question makes probable the truth of what we believe. So suppose some foundationalist offers an account of noninferential justification according to which a belief is noninferentially justified if it has some characteristic X (where X can stand for any sort of property including complex relational properties). BonJour then argues that the mere fact that the belief has X could not, even in principle, justify the believer in holding the belief. The believer would also need access to (justified belief that!) the belief in question has X and that beliefs of this sort (X beliefs) are likely to be true. At least one of these propositions could only be known through inference, and thus the putative noninferential justification is destroyed.

One must be careful in one's commitment to access requirements for justification lest the view become unintelligible. One can hardly expect an epistemologist to concede that any attempt to identify the conditions X that constitute justification will fail unless one supplements the account with conditions referring to actual or potential access to X. It is immediately clear that one couldn't even in principle satisfy this access internalist. If one tries to supplement X with a believer's having access to X, one simply creates a new condition Y (X plus access) which, according to the view, would itself need to be supplemented by the addition of access requirements to Y. But Y plus access (call it Z) will also be insufficient for justification—we will need to add access conditions to Z, and so on, ad infinitum. The most the access internalist could coherently assert is some sort of necessary connection between having justification and having actual or potential access to justification, where the access in question is not constitutive of the justification. But however one qualifies one's access requirements for justification, access internalism seems far too demanding a theory of epistemic justification. It seems to require of epistemic agents the capacity to form ever more complex justified metabeliefs about the justificatory status of beliefs below.

Traditional Foundationalism and Skepticism

The dissatisfaction with traditional foundationalism probably has as much to do with the threat of skepticism as with any more technical problem facing the view. If we understand noninferential justification in terms of infallible belief or acquaintance with a thought/world fit, on most versions of the traditional view there isn't much we are noninferentially justified in believing. If acquaintance is a real relation, it seems implausible, for example, to suppose that one is directly acquainted with facts about the past or the external world. The following sort of argument seems at least initially powerful:

It's possible that we seem to remember having done something X without having actually done it.

The justification we have for believing that we did X when we have a vivid “hallucinatory” memory would be the same as the justification we have for believing that we did X were we to veridically remember doing X.

The justification we have for believing that we did X when we have vivid “false” memory experience is not direct acquaintance with our having done X (acquaintance is a relation that requires the existence of its relata).

The justification we have for believing that we did X when we have a veridical memory experience is not direct acquaintance with our having done X.

An exactly parallel argument is available with respect to justification for believing propositions about the external world. Such justification never gets any better than the “evidence of our senses.” But,

The justification S has for believing some proposition about the physical world when suffering a vivid hallucinatory experience is the same as the justification S has for believing that proposition were S to have a phenomenologically indistinguishable veridical experience.

The justification S has when hallucinating is obviously not direct acquaintance with some feature of the physical world.

The justification S has in veridical experience is not direct acquaintance with some feature of the physical world.

If the above arguments are sound (they are certainly controversial), it is not entirely clear what will be left in the foundations of empirical justification. The classic empiricist view is that we have noninferentially justified empirical beliefs only about present conscious states. But it has been more than a little difficult to figure out how one can legitimately infer the rest of what we think we are justified in believing from such a limited set of premises. The problem is particularly acute if we accept the second clause of the principle of inferential justification. Given that clause, to advance beyond the foundations of justified belief we would inevitably need to employ nondeductive reasoning and, according to PIJ, that would ultimately require us to have noninferential justification for believing propositions describing probability connections between evidence and conclusions. As long as the relation of making probable is not defined in terms of frequency, as long as making probable is construed as a kind of “quasi‐logical relation” analogous to, but different from entailment, it may not be absurd to suppose that one can have noninferential a priori justification for believing that one set of propositions makes probable another. It is, however, an understatement to suggest that the view is problematic. 8

There is another source of dissatisfaction with the classic empiricist's suggestion that we identify noninferentially justified beliefs with beliefs about the character of present experience. Many would argue, on phenomenological grounds, that we rarely consider propositions describing the intrinsic character of experience. In sense experience our thought is almost always directed out of ourselves and on the existence of an external reality. It requires, the argument goes, considerable effort to turn “inward” to focus on appearance rather than external reality. If most people don't even have the beliefs that the traditional view regards as the only candidates for noninferentially justified beliefs, it seems that once again one faces an unpalatable, fairly extreme, skepticism.

Externalist Versions of Foundationalism

Contemporary externalists offer a refreshingly undemanding account of both non‐inferential and inferential justification. Just about all externalists reject the second clause of the principle of inferential justification. Moreover, noninferential justification is often understood in such a way as to allow for the possibility of a much broader foundation. Consider, for example, the best known version of externalism, Goldman's reliabilism (first set forth in Goldman 1979 ).

The fundamental idea behind reliabilism is strikingly simple. Justified beliefs are reliably produced beliefs. Justified beliefs are worth having because justified beliefs are probably true. The view is a version of foundationalism because it allows us to distinguish two importantly different sorts of justified beliefs—those that result from belief‐independent processes and those that result from belief‐dependent processes. The former are beliefs that are produced by “software” of the brain that takes as its “input” stimuli other than beliefs; the latter are beliefs produced by processes that take as their input at least some other beliefs. So, for example, it is possible that we have evolved in such a way that when prompted with certain sensory input, we immediately and unreflectively reach conclusions about external objects. And we may live in a world in which beliefs produced in such a way are usually true. Crude versions of reliabilism will regard such beliefs as noninferentially justified. Many of our beliefs, of course, result, at least in part, from prior beliefs we hold. We deduce and nondeductively infer a host of propositions describing the world in which we live. Again, on the crudest version of reliabilism, these belief‐dependent processes are reliable when the “output” beliefs are usually true provided that the input beliefs are true.

There are a host of questions that a reliabilist must answer in developing the details of the view. In the crude summary provided above we characterized the reliability of a belief‐independent process in terms of the frequency with which its output beliefs are true. But it takes little imagination to construct counterexamples to this naive a version of the view. Temporary paranoia might cause me to form two, and only two, beliefs about the malicious intentions of my friends, both of which happen to be true. But it hardly seems plausible to suppose that this coincidence makes for a 100 percent reliable belief‐forming process. Minimally, the reliabilist will turn to counterfactuals about the frequency with which output beliefs would be true were the process to produce indefinitely many of them. 9

If we settle the question of how to define reliability, we still need to determine whether the relevant concept of reliability should be relativized to circumstances. It seems obvious that a belief‐forming process might be entirely reliable in one environment, quite unreliable in another. Intuitively, even if the process nets us a majority of true beliefs, we don't want to concede that its operation in the “wrong” environment will result in justified beliefs. The obvious solution would be to define noninferential justification for a given believer in a given environment: S is noninferentially justified in believing P in C at t when S's belief that P in C at t is produced by a belief‐independent process that is reliable in C at t.

While perhaps the most influential, reliabilism is only one version of the externalist alternatives to traditional foundationalism. Armstrong ( 1973 ), for example, suggests the closely related view that some beliefs are noninferentially justified (basic) when they register accurately their subject matter the way an effective thermometer registers accurately the temperature. Although he would resent the suggestion that he is offering a theory of epistemic justification at all, Plantinga ( 1993 ) defines a concept of warrant in terms of beliefs produced by a cognitive apparatus that is properly functioning. He has his own distinctive theistic suggestion for how to understand proper function, but invites allies to try to define the notion in naturalistic terms (for example, in terms of evolutionary history). Plantinga's view is also a version of foundationalism because he holds that properly functioning belief‐producing mechanisms need not involve inference from justified belief.

The most striking feature of most versions of externalism is the way in which they open the door to the possibility of a vastly expanded class of noninferentially justified beliefs. According to the reliabilist, for example, it is never impossible for any belief to acquire noninferential justification. No matter what I believe, it is always in principle possible that the belief is produced by a reliable belief‐independent process. There might be a God who unbeknownst to me causes me to believe with complete conviction a host of true propositions and never causes me to believe a proposition that is false. Such divine inspiration would be a paradigm of a reliable belief‐forming process and the resulting beliefs would all be noninferentially justified. According to the reliabilist, whether or not a given belief is justified depends entirely on whether we are fortunate enough to live in a world in which our cognitive mechanisms produce in us beliefs that are largely successful in getting at the truth.

It is tempting to suppose that externalist versions of foundationalism only delay skeptical problems. Many externalists themselves seem to allow that one can legitimately worry that one has justification for believing that first‐level beliefs are justified. But it is another interesting (some would argue odd) feature of views like reliabilism that there really is no greater problem securing second‐level justification than there is for securing first‐level justification. If, for example, beliefs about the past produced by memory result from reliable belief‐independent processes, then the view implies that beliefs about the past are noninferentially justified. But if beliefs about the past are noninferentially justified then I can easily justify my belief that they are justified. All I need to do is remember that certain beliefs about the past turned out to be true when I relied on memory and employ a standard inductive argument to generalize that beliefs produced in this way are usually true. Of course the classic foundationalist will shudder at this shocking indifference to begging the question. They will protest that one cannot use memory to justify one's belief that memory is reliable! But if reliability really is the essence of justification, it's not clear why one can't study memory using memory to get justified beliefs about its reliability. The investigation into which belief‐forming processes are or are not reliable seems more a task for the cognitive psychologist than for the philosopher, but then perhaps this is why some contemporary epistemologists attempt to straddle the boundary between philosophy and empirical science.

Criticisms of Externalist Foundationalism

If classical foundationalism seemed to require too much in order for us to secure justified beliefs, externalist foundationalism strikes many as requiring too little. At least in a philosophical context, we are interested in having justification because we are interested in gaining a certain sort of assurance of truth. If we start to wonder whether our beliefs about external reality accurately represent that reality, it doesn't seem particularly useful to be told that we may have perfectly justified beliefs provided that they are produced in such a way that they usually accurately represent reality! The justification the philosopher seeks must be such that when one possesses it one's philosophical curiosity is satisfied.

If the primary dissatisfaction with externalism is the feeling that the traditional epistemological questions that have so interested philosophers have simply been redefined in such a way as to change the subject, there are also more technical objections that have been raised to the view. Perhaps the most striking involves a variation on a thought experiment used for a different purpose by Descartes. We'll illustrate the objection focusing again on reliabilism, but variations on the theme affect most externalist analyses of epistemic justification.

Consider a possible world (a kind of Matrix world) in which people are consistently and massively deceived with respect to external reality by a very powerful being. It seems intuitively plausible to suppose that the victims of demonic machination would have precisely the same sort of justification we have for believing (falsely as it turns out) what they do about the world around them. But by hypothesis the demon's victims' beliefs result from unreliable processes, while, we may suppose for the argument, our beliefs result from reliable processes. If the justificatory status of the demon‐world beliefs is the same as those of our world, then it just seems wrong to suppose that reliability is the essence of justification. Since his original paper advocating reliabilism, Goldman himself has struggled with how to respond to the intuitive force of this (and related) objections. After flirting (1986) with the idea of identifying the relevant reliability that defines justification as reliability in “normal” worlds (roughly, worlds in which certain fundamental beliefs we have about this world are true—whether or not they are true in the actual world!), Goldman ( 1988 ) eventually acknowledges two quite distinct concepts of justification: strong (defined by a hard‐core reliabilism in which we simply refuse to acknowledge that demon‐world inhabitants have epistemic justification) and weak (a less demanding concept of epistemic justification roughly defined in terms of meeting “community standards”).

Evidential Externalism

If one is convinced by the externalist that the traditional foundationalist has a concept of justification so demanding that it implies the implausible conclusion that the vast majority of our beliefs are unjustified, one might develop a kind of compromise. One might retain traditional foundationalism, replete with the principle of inferential justification, as capturing a kind of ideal epistemic justification that philosophers seek to attain, but which most people (and most philosophers, for that matter) fail to gain. To soften the blow, one might acknowledge a less demanding concept of epistemic justification that one might be able to satisfy through a kind of nonpropositional analogue of inference. Suppose, for example, that many of our beliefs about the external world are caused by the fact that we have had and are having certain sensations (together with a host of justified background beliefs, most of which remain dispositional). Suppose further that we rarely form beliefs about the character of these sensations, have long since forgotten many of the relevant past experiences (that nevertheless still exert their causal influence), and, of course, rarely, if ever, consciously construct some argument for the ordinary beliefs and expectations we constantly form about the world around us. The facts about sensations that causally contribute to our beliefs about the world are also truth‐makers for propositions (whether we entertain the propositions or not) and it might be the case that the conjunction of propositions made true by the causes of our belief, together with the enormous structure of propositions dispositionally believed that form our epistemic “background,” do make probable (via some sort of legitimate reasoning the epistemologist struggles, usually in vain, to uncover) common‐sense, everyday beliefs. Perhaps we can acknowledge a kind of “unreflective” epistemic justification that we might possess provided that our internal states (including dispositional beliefs and noncognitive states like sensation) satisfy the conditions described above.

Susan Haack ( 1993 ) develops a version of this view but takes a very liberal attitude with respect to what proposition we can employ as the propositional counterpart to sensation. She seems to suggest that we can take the relevant proposition describing a sensation to be one that describes it as the sensation usually produced by a certain physical object under certain conditions. Even if the skeptic allows us a less demanding concept of epistemic justification, that skeptic will no doubt balk at the suggestion that we can take evidential connections between propositions formed this way to be the truth‐makers for claims about epistemic justification. One does need criteria for choosing the propositional counterparts of sensations playing their causal role, but if it is facts that are both causes and truth‐makers for propositions, one can identify the relevant evidential proposition that corresponds to a sensation as the one made true by the fact about the sensation that is causally efficacious in producing the belief in whose epistemic status we are interested.

The above account might seem to be only a minor variation on the concept of epistemic justification defined by reliabilism. Whether this is so depends on how one understands evidential connections. If making probable is a kind of quasi‐logical relation holding between propositions (perhaps even holding necessarily) then the concept of unreflective justification sketched above will be able to resolve the demon‐world objection to reliabilism. The internal causes of belief in the demon world are, by hypothesis, the same as the internal causes of belief in “normal” worlds. The evidential propositional counterparts to the sensory states will be the same, and the justificatory status of the resulting beliefs will be the same. Of course, there may (relative to what we know reflectively) be no evidential connections between the propositions that form our justified background beliefs, the propositions made true by sensation, and the propositions that constitute the conclusions of our common‐sense beliefs, but should that be the case, skepticism wins the day both with respect to demanding and undemanding concepts of epistemic justification.

Contextualism

While the evidential externalist I discussed above is prepared to distinguish more and less demanding standards of justification, the contextualist , for example, Annis ( 1978 ), allows for standards to “float” where the requirements for justified belief are determined in part by the context of inquiry. Recent versions of the view are most often accounts of knowledge. So, for example, Lewis ( 1996 ) suggests that S knows that P when S has a true belief that P where S's evidence eliminates all relevant alternatives to P. What makes the view contextualist is that relevancy is determined by context, including such subjective factors as whether or not the believer is taking seriously the possibility of an alternative. The view is supposed to have the virtue of accommodating both common sense and skepticism—knowledge claims in ordinary contexts will remain true, while in philosophical contexts the skeptic is likely to win the day by forcing us to consider (and thus make relevant) various skeptical scenarios. An analogous view about requirements for justification might allow that one only needs justification for believing certain premises crucial to our reaching conclusions when these background beliefs come under challenge. In ordinary contexts where everyone is happy to allow the truth of our premises and the legitimacy of our reasoning, we can get justified beliefs without having to do what would be necessary were these to come under skeptical challenge.

There is, no doubt, a grain of truth in the contextualist's account of our ordinary, everyday assessments of justification and knowledge. In the context of assessing the justification available for accepting a scientific theory, one simply doesn't worry about the justification we have for believing in the existence of a past or an external world. We assume in the context of such a discussion that we have certain knowledge and that certain forms of reasoning are legitimate, and go on to ask whether on these assumptions, we can legitimately infer the truth of the theory in which we are interested. Philosophers themselves often raise certain objections to common sense beliefs in one philosophical context, only to assume the truth of those very beliefs in a different philosophical context. 10 Monks debating some esoteric proposition concerning the details of their theology may well take for granted the reliability of the Old Testament as a source of truth, presumably knowing full well that should they end up debating an atheist they would need to take a quite different approach.

None of this seems to provide any real support for an interesting form of contextualism, either about knowledge or justification. That we will often “bracket” one set of issues in the context of addressing another, that we will often be interested in seeing what follows from a given set of assumptions, setting aside our ability to “satisfy reason” with respect to those assumptions, is perfectly compatible with our recognizing that in the end our reasons for accepting our conclusions are never really any better than our reasons for accepting the host of background assumptions that remain in the background until we decide to focus our attention upon them. 11

Coherentism

Despite the radical differences among the traditional foundationalists and their more recent externalist counterparts, members of both camps typically share a common conception of the foundational structure of epistemic justification—they are common allies in the fight against coherence theories of epistemic justification.

The coherence theorist rejects the foundationalist's conception of justification as linear . Convinced that there is no escape from the “circle of beliefs”, the coherence theorist argues that we must understand the epistemic justification for a belief in terms of the way in which the proposition believed coheres with other propositions believed. We can distinguish pure and impure coherence theories of justification. A pure coherence theory takes the justification of every belief to be a matter of coherence. An impure theory restricts the thesis to a subclass of beliefs. BonJour ( 1985 ), for example, defended a coherence theory of epistemic justification for empirical beliefs only, but there is nothing in principle to prevent a coherence theorist from restricting the theory to an even more narrow subclass of beliefs.

The vast majority of philosophers who support a coherence theory of justification take the relevant beliefs with which a given justified belief must cohere to be those present in a single individual. What justifies S in believing P is that P coheres with some set of propositions that S occurrently or dispositionally believes (or would believe were S to reflect in a certain way). What justifies you in believing P is P's coherence with other propositions you do or would believe. But while epistemic justification relativized to an individual's belief system is the norm for coherence theories, one finds at least some interest in what we might call a social coherence theory. Roughly, the idea is that what justifies S in believing P is a matter not just of what S believes, but of what others in the community believe. A very crude social coherence theory of epistemic justification might hold that S is justified in believing P only if P coheres with the propositions believed by all or most members of S's community. Because one can distinguish as many different communities as one likes, epistemic justification on this view must always be relativized to a given community. For simplicity, we will focus on the kind of coherence theories that relativize epistemic justification to an individual's belief system, but most of what we say will apply mutatis mutandis to other versions of the view.

Once we are clear about which beliefs a given belief must cohere with in order to be epistemically justified, we'll need more information from the coherence theorist about what constitutes coherence. Often the coherence theorist will begin by claiming that coherence must minimally involve logical consistency, but go on to concede that consistency is far too weak a requirement to constitute the mainstay of coherence. One can imagine a person with a thousand beliefs, none of which have anything to do with any of the others but where each proposition believed is consistent with the conjunction of the others. Such a belief system hardly seems a paradigm of coherence, and we would be reluctant to concede that each has epistemic justification.

In an interesting argument, Foley ( 1979 ) has argued persuasively that consistency among the propositions one believes is not even a necessary condition for the beliefs' being justified. Focusing on lottery‐type situations, Foley argues that we can easily think of a set of inconsistent beliefs each of which is perfectly justified. If there are a thousand people in a lottery that I know to be fair, I can justifiably believe of each participant that he or she will lose and also justifiably believe that not all of them will lose. None of these beliefs is logically consistent with the conjunction of the rest, but each is justified. So the coherence theorist is wrong to tell us that a belief of ours is epistemically justified only if it is consistent with the rest of what we believe. A closely related problem concerns the possibility of admitting into one's belief system a necessary falsehood F. If one believes even one necessary falsehood, then none of one's beliefs will be consistent with the rest of what one believes; the conjunction of a necessary falsehood with any other proposition is itself a necessary falsehood. It seems more than a little harsh, however, to let one philosophical or mathematical error of this sort destroy the possibility of there being any epistemic justification for believing any proposition.

Coherence theorists are wary of requiring too much for the coherence of a belief system. So, for example, one might initially suppose that a model of a coherent belief system might be one in which each proposition believed is entailed by the conjunction of the rest. But one might also worry that such a requirement is far too difficult to come by. In one sense, however, the worry is misplaced. It is actually extremely easy to satisfy the requirement. Indeed, if we include dispositional beliefs, I can confidently claim to have a belief system in which each of my beliefs is entailed by the rest of what I believe. And the same is, or should be, true of everyone who has taken and remembers a course in elementary logic. One of the truth‐functional connectives we all learned was material implication. As long as we know its truth functional definition, we know that if P is true and Q is true then it is true that P materially implies Q and true that Q materially implies P. Consequently, I assume that if we believe P and believe Q, we will also believe (at least dispositionally) that P materially implies Q and that Q materially implies P. But then for any two propositions P and Q that I happen to believe, there will be in my belief system propositions entailing each. P will be entailed by (Q and Q materially implies P) and Q will be entailed by (P and P materially implies Q). The coherence theorist will no doubt be tempted to reply that the belief in the conditionals is entirely parasitic upon the prior beliefs in P and Q, but once one abandons a linear conception of justification, it's not clear what sort of epistemic priority P and Q are supposed to have just because they may have preceded the belief that P is true if and only if Q is true.

Ironically, perhaps probabilistic connections provide a stronger “glue” for coherence than logical relations. So a coherence theorist is likely to claim that a system of beliefs increases its coherence the more the propositions believed stand in probabilistic connections with each other. Explanatory coherence theorists emphasize the importance of having a belief system in which one maximizes the number of propositions believed where one has within one's belief system propositions that can explain the propositions believed. It's difficult, however, to regard entailment as anything other than the limit of making probable, and if it is too easy to come by a belief system in which each proposition believed is entailed by the rest, it's hard to see how one can avoid the problem by emphasizing probability.

There are enough powerful arguments against coherence theories of justification that one need not turn to more problematic concerns. And some objections to a coherence theory do seem to miss the mark. So, for example, some seem to be concerned with the fact that the coherence theorist embraces a radical relativization of justification. But any plausible account of epistemic justification will acknowledge that one person S can be justified in believing P, while another R is justified in believing not‐P. The traditional foundationalist will no doubt trace the difference between the justificatory status of S and R's beliefs to differences in their memories of past experiences, but it is still the case that radical relativization of justification should be embraced as much by traditional foundationalists as by coherentists.

There is, perhaps, the vague concern that a coherence theory of justification makes one's choice of what to believe far too subjective. I want to know what to believe and the coherence theorist tells me to come up with a coherent set of beliefs. But for every coherent set of propositions I entertain, I can think of another set inconsistent with the first but just as internally coherent. Won't this make the epistemic choice of what to believe implausibly arbitrary? If a theory of justification is to give one guidance, and if one were to somehow start one's deliberations about what to believe with no beliefs at all, then it would seem that the coherence theorist gives one no advice at all concerning what to believe. But we are no doubt simply caused to believe firmly certain propositions, and given that we find ourselves with certain beliefs and are trying to determine whether or not to hold still others, it's not clear that the coherence theorist leaves us with no guidance.

A similar response can be made to those who worry that the coherence theorist cuts us off from the world that makes true or false our beliefs. Nothing in the theory, however, precludes the possibility of our beliefs being caused by features of a belief‐independent world. The epistemological coherence theory holds only that whatever the cause of our beliefs, their epistemic status is a function solely of coherence. 12

Perhaps the most devastating criticism of coherence theories was, ironically, put forth by BonJour in the course of defending the view. Earlier we talked about differences between internalists and externalists. One version of internalism (we might call it inferential internalism) insists that evidential connections between propositions believed does nothing to secure justification for the believer unless the believer has access to the fact that the evidential connections hold. We can then distinguish two radically different versions of coherentism. On one version, a belief is epistemically justified provided that it forms a part of a coherent belief system. On the other, a belief is epistemically justified provided that the believer is aware that (has a justified belief that) the belief coheres with the rest of what is believed. The first version of coherentism seems vulnerable to devastating counterexamples. If a person believes a set of propositions that cohere wonderfully when the person has no way of discovering the inferential connections, in what sense are the beliefs justified? Suppose, for example, that I decide to believe every proposition expressed by the fourth sentence of every paragraph in a very sophisticated physics text. Through a miraculous coincidence the propositions I believe cohere wonderfully. Each is made probable by some conjunction of the others. I, however, have no clue as to what the evidential connections are. Would anyone suppose that my good fortune translates into justification?

If we embrace instead access coherentism, then coherentists face the very regress that traditional foundationalists tried so desperately to avoid. To justifiably believe that our beliefs cohere we would need to know first what we believe and second that the propositions believed stand in the appropriate evidential relations. But as coherentists we have no foundations to fall back on. We can't just give ourselves privileged access to propositions describing our own belief states. Our only access to what we believe is through a coherence we discover between our belief that we have certain beliefs and the rest of what we believe. But to discover this coherence we will once again be forced to discover what we believe, and so on, ad infinitum. An equally vicious regress seems to plague any attempt to discover evidential connections. To justify our belief that a given evidential connection obtains, we would need to discover coherence between our belief that the evidential connection obtains and the rest of what we believe. But discovering that coherence would require that we discover another coherence between our belief about coherence and the rest of what we believe, and so on, ad infinitum.

The basic problem facing access coherence theorists is simple. As pure coherence theorists they have no business giving themselves unproblematic access to any facts about the internal or external world, or the world of logical connections. If there really is a “veil” of belief, then beliefs themselves are hidden from us by metabeliefs, which are hidden from us by metametabeliefs, and so on, ad infinitum. Whenever we attempt to get anything before our consciousness we are led on an endless goose chase toward higher‐ and higher‐level metabeliefs.

Mixed Theories

Susan Haack ( 1993 ), Roderick Chisholm ( 1989 ), Ernest Sosa ( 1991 ), and others have suggested that we don't need to choose between foundationalism and coherentism. We can incorporate elements of both. Haack's crossword puzzle metaphor is perhaps the most vivid illustration of the idea. In a crossword puzzle, we are given an initial clue that may lead us to a tentative conclusion about the correct entry in the puzzle. But it is only when our tentative entry “fits” with the other entries we try that we feel confident that we have the correct solution to the puzzle. According to Haack, experience provides a kind of foundational clue with respect to truth, but coherence (fit) is necessary to raise the level of initial credibility to that of epistemic justification. Sosa allows for a kind of animal knowledge resulting from reliable belief‐forming processes (where reliability is relativized to internal and external circumstances) but insists that it is only when one's belief that one has animal knowledge coheres with the rest of one's beliefs that we can turn animal knowledge into reflective knowledge. Although he is one of the most prominent foundationalists, Chisholm allowed that coherence (concurrence) among propositions believed might be one way to raise the epistemic status of those beliefs (69–71).

Such views obviously need to be evaluated carefully, but it is not clear that any concept of justification purportedly captured by the mixed theory cannot be captured by a more straightforward foundationalism. If we have foundational evidence E1 for P1, foundational evidence E2 for P2, and foundational evidence E3 for P3, then instead of insisting that it is coherence among P1, P2, and P3 that raises the epistemic justification for believing each one, why not simply claim that it is the conjunction of E1, E2, and E3 that constitutes a foundational justification for believing each of P1, P2, and P3, where the conjunction of evidence makes more probable P1, for example, than E1 does alone?

A survey of this sort can at best suggest the rich diversity of views about the nature of epistemic justification and the equally rich diversity of objections those views face. In illustrating many of these views and objections I have painted with a very broad stroke. Moreover, there are a host of interesting variations on the views I did discuss that have been defended by able philosophers one would have liked to mention in a survey of this sort. Painting with a broad stroke can still give one a useful “big” picture, and this is all I hoped to accomplish in the preceding remarks.

See Cohen ( 1984 ) for a defense of the idea that a connection to truth lies at the heart of epistemic justification.

We often speak of having some epistemic justification for believing a proposition in contrast with having justification simpliciter. One can have some epistemic justification for believing P when the justification does not even make P more likely to be true than not—it simply increases the probability of P's being true. In what follows I'll almost always be talking about justification as “all‐things‐considered justification” and will use the term in such a way that one has justification for believing P only if, all things considered, the justification makes P more likely than not to be true.

I have argued elsewhere that our understanding of the “ought” of practical rationality and morality is in fact parasitic upon our understanding of the epistemic ought. It is implausible to understand what it is rational or moral to do in terms of the actual consequences that would result from alternatives. Practical and moral reasons seem to have more to do with what one is epistemically justified in believing about consequences.

For a more detailed discussion of this issue, see Fumerton ( 2001 ).

The example may not be fair. It is far from clear that anyone really accepts as legitimate an argument whose premise describes a lifeline and whose conclusion describes length of life. Such arguments may always be enthymemes. The question then becomes whether it is still plausible to claim that one cannot justifiably accept the conclusion of any argument without justifiably believing that there exists the relevant connection between premise and conclusion. I think one can make the case that it is.

There is, however, nothing to prevent an acquaintance theorist from allowing that one can have noninferential justification for believing P that does not entail P's truth. It may be that one can be noninferentially justified in believing P in virtue of being directly acquainted with a fact very similar to, but ultimately different from the fact that P. For an attempt to develop this view in more detail see Fumerton ( 1985 ).

See, for example, Goodman ( 1978 ) and Putnam ( 1988 ).

One of the earliest attempts to construe probability as a relation that holds necessarily between certain propositions was Keynes ( 1921 ).

Or, one could replace talk of frequency in defining reliability with some other notion. Goldman toys with the idea of understanding reliability in terms of an undefined notion of propensity to produce true beliefs.

David Hume ( 1888 ), for example, attacked relentlessly the legitimacy of inductive reasoning and the rationality of belief in an external world, only to assume both in the context of investigating the subject matter of moral judgments.

For a more detailed discussion of contextualism see Moser 1985 , chap. 2.

There is also a coherence theory of truth that might seem a natural ally of the coherence theory of justification. The problems facing a coherence theory of justification, however, pale in comparison to those facing the coherence theory of truth. See Fumerton ( 2001 ).

Annis, David ( 1978 ). “ A Contextualist Theory of Epistemic Justification. ” American Philosophical Quarterly 15: 213–219.

Google Scholar

Armstrong, David ( 1973 ). Belief, Truth and Knowledge . London: Cambridge University Press.

Google Preview

Ayer, A. J. ( 1956 ). The Problem of Knowledge . Edinburgh: Penguin.

BonJour, Laurence ( 1985 ). The Structure of Empirical Knowledge . Cambridge, Mass.: Harvard University Press.

Cohen, Stewart ( 1984 ). “ Justification and Truth. ” Philosophical Studies 46: 279–296. 10.1007/BF00372907

Chisholm, Roderick ( 1989 ). Theory of Knowledge , 3rd ed. Englewood Cliffs, N.J.: Prentice‐Hall.

Foley, Richard ( 1979 ). “ Justified Inconsistent Beliefs. ” American Philosophical Quarterly 16: 147–158.

——. ( 1987 ). The Theory of Epistemic Rationality . Cambridge: Harvard University Press.

Fumerton, Richard ( 1985 ). Metaphysical and Epistemological Problems of Perception . Lincoln: University of Nebraska Press.

——. ( 2001 ). “Epistemic Justification and Normativity.” In Knowledge, Truth and Obligation: Essays on Epistemic Responsibility and the Ethics of Belief , ed. Matthias Steup. Oxford: Oxford University Press.

——. ( 2002 ). Truth and Correspondence . Boston: Rowman & Littlefield.

Goldman, Alvin ( 1979 ). “What Is Justified Belief?” In Justification and Knowledge , ed. George Pappas , 1–23. Dordrecht: Reidel.

——. ( 1986 ). Epistemology and Cognition . Cambridge: Harvard University Press.

——. ( 1988 ). “Strong and Weak Justification.” In Philosophical Perspectives vol. 2: Epistemology , ed. James Tomberlin , 51–69. Atascadero, Calif.: Ridgeview.

Goodman, Nelson ( 1978 ). Ways of World Making . Indianapolis: Hackett.

Haack, Susan ( 1993 ). Evidence and Inquiry . Oxford: Blackwell.

Hume, David ( 1888 ). A Treatise of Human Nature , ed. L. A. Selby‐Bigge . London: Oxford University Press.

Keynes, John ( 1921 ). A Treatise on Probability . London: Macmillan.

Klein, Peter (1999). “Human Knowledge and the Infinite Regress of Reasons.” In Philosophical Perspectives , vol. 13, ed. James Tomberlin , 297–325.

Lehrer, Keith ( 1974 ). Knowledge . Oxford: Oxford University Press.

Lewis, David ( 1996 ). “ Elusive Knowledge. ” Australian Journal of Philosophy 74: 4.

Moser, Paul ( 1985 ). Empirical Justification . Dordrecht: Reidel.

Plantinga, Alvin ( 1992 ). “Justification in the 20th Century.” In Philosophical Issues , vol. 2: Rationality in Epistemology , ed. Enrique Villanueva , 43–78. Atascadero, Calif.: Ridgeview.

——. ( 1993 ). Warrant and Proper Function . Oxford: Oxford University Press. 10.1093/oxfordhb/0195078640.001.0001

Putnam, Hilary ( 1988 ). Representation and Reality . Cambridge, Mass.: MIT Press.

Russell, Bertrand ( 1959 ). The Problems of Philosophy . Oxford: Oxford University Press.

——. ( 1984 ). Theory of Knowledge: The 1913 Manuscript , ed. E. Eames . London: Allen and Unwin.

Sellars, Wilfrid ( 1963 ). Science, Perception and Reality . London: Routledge and Kegan Paul.

Sosa, Ernest ( 1991 ). Knowledge in Perspective . Cambridge: Cambridge University Press.

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What is and How to Write a Good Hypothesis in Research?

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One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

Language Editing Plus

Elsevier’s Language Editing Plus service can help ensure that your research hypothesis is well-designed, and articulates your research and conclusions. Our most comprehensive editing package, you can count on a thorough language review by native-English speakers who are PhDs or PhD candidates. We’ll check for effective logic and flow of your manuscript, as well as document formatting for your chosen journal, reference checks, and much more.

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

This is awesome.Wow.

It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

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Explained well and in simple terms. Quick read! Thank you

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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General Education

Think about something strange and unexplainable in your life. Maybe you get a headache right before it rains, or maybe you think your favorite sports team wins when you wear a certain color. If you wanted to see whether these are just coincidences or scientific fact, you would form a hypothesis, then create an experiment to see whether that hypothesis is true or not.

But what is a hypothesis, anyway? If you’re not sure about what a hypothesis is--or how to test for one!--you’re in the right place. This article will teach you everything you need to know about hypotheses, including: 

• Defining the term “hypothesis” 
• Providing hypothesis examples 
• Giving you tips for how to write your own hypothesis

So let’s get started!

What Is a Hypothesis?

Merriam Webster defines a hypothesis as “an assumption or concession made for the sake of argument.” In other words, a hypothesis is an educated guess . Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it’s true or not. Keep in mind that in science, a hypothesis should be testable. You have to be able to design an experiment that tests your hypothesis in order for it to be valid. 

As you could assume from that statement, it’s easy to make a bad hypothesis. But when you’re holding an experiment, it’s even more important that your guesses be good...after all, you’re spending time (and maybe money!) to figure out more about your observation. That’s why we refer to a hypothesis as an educated guess--good hypotheses are based on existing data and research to make them as sound as possible.

Hypotheses are one part of what’s called the scientific method .  Every (good) experiment or study is based in the scientific method. The scientific method gives order and structure to experiments and ensures that interference from scientists or outside influences does not skew the results. It’s important that you understand the concepts of the scientific method before holding your own experiment. Though it may vary among scientists, the scientific method is generally made up of six steps (in order):

• Observation
• Asking questions
• Forming a hypothesis
• Analyze the data
• Communicate your results

You’ll notice that the hypothesis comes pretty early on when conducting an experiment. That’s because experiments work best when they’re trying to answer one specific question. And you can’t conduct an experiment until you know what you’re trying to prove!

Independent and Dependent Variables 

After doing your research, you’re ready for another important step in forming your hypothesis: identifying variables. Variables are basically any factor that could influence the outcome of your experiment . Variables have to be measurable and related to the topic being studied.

There are two types of variables:  independent variables and dependent variables. I ndependent variables remain constant . For example, age is an independent variable; it will stay the same, and researchers can look at different ages to see if it has an effect on the dependent variable. 

Speaking of dependent variables... dependent variables are subject to the influence of the independent variable , meaning that they are not constant. Let’s say you want to test whether a person’s age affects how much sleep they need. In that case, the independent variable is age (like we mentioned above), and the dependent variable is how much sleep a person gets. 

Variables will be crucial in writing your hypothesis. You need to be able to identify which variable is which, as both the independent and dependent variables will be written into your hypothesis. For instance, in a study about exercise, the independent variable might be the speed at which the respondents walk for thirty minutes, and the dependent variable would be their heart rate. In your study and in your hypothesis, you’re trying to understand the relationship between the two variables.

Elements of a Good Hypothesis

The best hypotheses start by asking the right questions . For instance, if you’ve observed that the grass is greener when it rains twice a week, you could ask what kind of grass it is, what elevation it’s at, and if the grass across the street responds to rain in the same way. Any of these questions could become the backbone of experiments to test why the grass gets greener when it rains fairly frequently.

As you’re asking more questions about your first observation, make sure you’re also making more observations . If it doesn’t rain for two weeks and the grass still looks green, that’s an important observation that could influence your hypothesis. You'll continue observing all throughout your experiment, but until the hypothesis is finalized, every observation should be noted.

Finally, you should consult secondary research before writing your hypothesis . Secondary research is comprised of results found and published by other people. You can usually find this information online or at your library. Additionally, m ake sure the research you find is credible and related to your topic. If you’re studying the correlation between rain and grass growth, it would help you to research rain patterns over the past twenty years for your county, published by a local agricultural association. You should also research the types of grass common in your area, the type of grass in your lawn, and whether anyone else has conducted experiments about your hypothesis. Also be sure you’re checking the quality of your research . Research done by a middle school student about what minerals can be found in rainwater would be less useful than an article published by a local university.

Writing Your Hypothesis

Once you’ve considered all of the factors above, you’re ready to start writing your hypothesis. Hypotheses usually take a certain form when they’re written out in a research report.

When you boil down your hypothesis statement, you are writing down your best guess and not the question at hand . This means that your statement should be written as if it is fact already, even though you are simply testing it.

The reason for this is that, after you have completed your study, you'll either accept or reject your if-then or your null hypothesis. All hypothesis testing examples should be measurable and able to be confirmed or denied. You cannot confirm a question, only a statement! 

In fact, you come up with hypothesis examples all the time! For instance, when you guess on the outcome of a basketball game, you don’t say, “Will the Miami Heat beat the Boston Celtics?” but instead, “I think the Miami Heat will beat the Boston Celtics.” You state it as if it is already true, even if it turns out you’re wrong. You do the same thing when writing your hypothesis.

Additionally, keep in mind that hypotheses can range from very specific to very broad.  These hypotheses can be specific, but if your hypothesis testing examples involve a broad range of causes and effects, your hypothesis can also be broad.  

The Two Types of Hypotheses

Now that you understand what goes into a hypothesis, it’s time to look more closely at the two most common types of hypothesis: the if-then hypothesis and the null hypothesis.

#1: If-Then Hypotheses

First of all, if-then hypotheses typically follow this formula:

If ____ happens, then ____ will happen.

The goal of this type of hypothesis is to test the causal relationship between the independent and dependent variable. It’s fairly simple, and each hypothesis can vary in how detailed it can be. We create if-then hypotheses all the time with our daily predictions. Here are some examples of hypotheses that use an if-then structure from daily life: 

• If I get enough sleep, I’ll be able to get more work done tomorrow.
• If the bus is on time, I can make it to my friend’s birthday party. 
• If I study every night this week, I’ll get a better grade on my exam. 

In each of these situations, you’re making a guess on how an independent variable (sleep, time, or studying) will affect a dependent variable (the amount of work you can do, making it to a party on time, or getting better grades). 

You may still be asking, “What is an example of a hypothesis used in scientific research?” Take one of the hypothesis examples from a real-world study on whether using technology before bed affects children’s sleep patterns. The hypothesis read s:

“We hypothesized that increased hours of tablet- and phone-based screen time at bedtime would be inversely correlated with sleep quality and child attention.”

It might not look like it, but this is an if-then statement. The researchers basically said, “If children have more screen usage at bedtime, then their quality of sleep and attention will be worse.” The sleep quality and attention are the dependent variables and the screen usage is the independent variable. (Usually, the independent variable comes after the “if” and the dependent variable comes after the “then,” as it is the independent variable that affects the dependent variable.) This is an excellent example of how flexible hypothesis statements can be, as long as the general idea of “if-then” and the independent and dependent variables are present.

#2: Null Hypotheses

Your if-then hypothesis is not the only one needed to complete a successful experiment, however. You also need a null hypothesis to test it against. In its most basic form, the null hypothesis is the opposite of your if-then hypothesis . When you write your null hypothesis, you are writing a hypothesis that suggests that your guess is not true, and that the independent and dependent variables have no relationship .

One null hypothesis for the cell phone and sleep study from the last section might say: 

“If children have more screen usage at bedtime, their quality of sleep and attention will not be worse.” 

In this case, this is a null hypothesis because it’s asking the opposite of the original thesis! 

Conversely, if your if-then hypothesis suggests that your two variables have no relationship, then your null hypothesis would suggest that there is one. So, pretend that there is a study that is asking the question, “Does the amount of followers on Instagram influence how long people spend on the app?” The independent variable is the amount of followers, and the dependent variable is the time spent. But if you, as the researcher, don’t think there is a relationship between the number of followers and time spent, you might write an if-then hypothesis that reads:

“If people have many followers on Instagram, they will not spend more time on the app than people who have less.”

In this case, the if-then suggests there isn’t a relationship between the variables. In that case, one of the null hypothesis examples might say:

“If people have many followers on Instagram, they will spend more time on the app than people who have less.”

You then test both the if-then and the null hypothesis to gauge if there is a relationship between the variables, and if so, how much of a relationship. 

4 Tips to Write the Best Hypothesis

If you’re going to take the time to hold an experiment, whether in school or by yourself, you’re also going to want to take the time to make sure your hypothesis is a good one. The best hypotheses have four major elements in common: plausibility, defined concepts, observability, and general explanation.

#1: Plausibility

At first glance, this quality of a hypothesis might seem obvious. When your hypothesis is plausible, that means it’s possible given what we know about science and general common sense. However, improbable hypotheses are more common than you might think. 

Imagine you’re studying weight gain and television watching habits. If you hypothesize that people who watch more than  twenty hours of television a week will gain two hundred pounds or more over the course of a year, this might be improbable (though it’s potentially possible). Consequently, c ommon sense can tell us the results of the study before the study even begins.

Improbable hypotheses generally go against  science, as well. Take this hypothesis example: 

“If a person smokes one cigarette a day, then they will have lungs just as healthy as the average person’s.” 

This hypothesis is obviously untrue, as studies have shown again and again that cigarettes negatively affect lung health. You must be careful that your hypotheses do not reflect your own personal opinion more than they do scientifically-supported findings. This plausibility points to the necessity of research before the hypothesis is written to make sure that your hypothesis has not already been disproven.

#2: Defined Concepts

The more advanced you are in your studies, the more likely that the terms you’re using in your hypothesis are specific to a limited set of knowledge. One of the hypothesis testing examples might include the readability of printed text in newspapers, where you might use words like “kerning” and “x-height.” Unless your readers have a background in graphic design, it’s likely that they won’t know what you mean by these terms. Thus, it’s important to either write what they mean in the hypothesis itself or in the report before the hypothesis.

Here’s what we mean. Which of the following sentences makes more sense to the common person?

If the kerning is greater than average, more words will be read per minute.

If the space between letters is greater than average, more words will be read per minute.

For people reading your report that are not experts in typography, simply adding a few more words will be helpful in clarifying exactly what the experiment is all about. It’s always a good idea to make your research and findings as accessible as possible. 

Good hypotheses ensure that you can observe the results. 

#3: Observability

In order to measure the truth or falsity of your hypothesis, you must be able to see your variables and the way they interact. For instance, if your hypothesis is that the flight patterns of satellites affect the strength of certain television signals, yet you don’t have a telescope to view the satellites or a television to monitor the signal strength, you cannot properly observe your hypothesis and thus cannot continue your study.

Some variables may seem easy to observe, but if you do not have a system of measurement in place, you cannot observe your hypothesis properly. Here’s an example: if you’re experimenting on the effect of healthy food on overall happiness, but you don’t have a way to monitor and measure what “overall happiness” means, your results will not reflect the truth. Monitoring how often someone smiles for a whole day is not reasonably observable, but having the participants state how happy they feel on a scale of one to ten is more observable. 

In writing your hypothesis, always keep in mind how you'll execute the experiment.

#4: Generalizability 

Perhaps you’d like to study what color your best friend wears the most often by observing and documenting the colors she wears each day of the week. This might be fun information for her and you to know, but beyond you two, there aren’t many people who could benefit from this experiment. When you start an experiment, you should note how generalizable your findings may be if they are confirmed. Generalizability is basically how common a particular phenomenon is to other people’s everyday life.

Let’s say you’re asking a question about the health benefits of eating an apple for one day only, you need to realize that the experiment may be too specific to be helpful. It does not help to explain a phenomenon that many people experience. If you find yourself with too specific of a hypothesis, go back to asking the big question: what is it that you want to know, and what do you think will happen between your two variables?

Hypothesis Testing Examples

We know it can be hard to write a good hypothesis unless you’ve seen some good hypothesis examples. We’ve included four hypothesis examples based on some made-up experiments. Use these as templates or launch pads for coming up with your own hypotheses.

Experiment #1: Students Studying Outside (Writing a Hypothesis)

You are a student at PrepScholar University. When you walk around campus, you notice that, when the temperature is above 60 degrees, more students study in the quad. You want to know when your fellow students are more likely to study outside. With this information, how do you make the best hypothesis possible?

You must remember to make additional observations and do secondary research before writing your hypothesis. In doing so, you notice that no one studies outside when it’s 75 degrees and raining, so this should be included in your experiment. Also, studies done on the topic beforehand suggested that students are more likely to study in temperatures less than 85 degrees. With this in mind, you feel confident that you can identify your variables and write your hypotheses:

If-then: “If the temperature in Fahrenheit is less than 60 degrees, significantly fewer students will study outside.”

Null: “If the temperature in Fahrenheit is less than 60 degrees, the same number of students will study outside as when it is more than 60 degrees.”

These hypotheses are plausible, as the temperatures are reasonably within the bounds of what is possible. The number of people in the quad is also easily observable. It is also not a phenomenon specific to only one person or at one time, but instead can explain a phenomenon for a broader group of people.

To complete this experiment, you pick the month of October to observe the quad. Every day (except on the days where it’s raining)from 3 to 4 PM, when most classes have released for the day, you observe how many people are on the quad. You measure how many people come  and how many leave. You also write down the temperature on the hour. 

After writing down all of your observations and putting them on a graph, you find that the most students study on the quad when it is 70 degrees outside, and that the number of students drops a lot once the temperature reaches 60 degrees or below. In this case, your research report would state that you accept or “failed to reject” your first hypothesis with your findings.

Experiment #2: The Cupcake Store (Forming a Simple Experiment)

Let’s say that you work at a bakery. You specialize in cupcakes, and you make only two colors of frosting: yellow and purple. You want to know what kind of customers are more likely to buy what kind of cupcake, so you set up an experiment. Your independent variable is the customer’s gender, and the dependent variable is the color of the frosting. What is an example of a hypothesis that might answer the question of this study?

Here’s what your hypotheses might look like: 

If-then: “If customers’ gender is female, then they will buy more yellow cupcakes than purple cupcakes.”

Null: “If customers’ gender is female, then they will be just as likely to buy purple cupcakes as yellow cupcakes.”

This is a pretty simple experiment! It passes the test of plausibility (there could easily be a difference), defined concepts (there’s nothing complicated about cupcakes!), observability (both color and gender can be easily observed), and general explanation ( this would potentially help you make better business decisions ).

Experiment #3: Backyard Bird Feeders (Integrating Multiple Variables and Rejecting the If-Then Hypothesis)

While watching your backyard bird feeder, you realized that different birds come on the days when you change the types of seeds. You decide that you want to see more cardinals in your backyard, so you decide to see what type of food they like the best and set up an experiment. 

However, one morning, you notice that, while some cardinals are present, blue jays are eating out of your backyard feeder filled with millet. You decide that, of all of the other birds, you would like to see the blue jays the least. This means you'll have more than one variable in your hypothesis. Your new hypotheses might look like this: 

If-then: “If sunflower seeds are placed in the bird feeders, then more cardinals will come than blue jays. If millet is placed in the bird feeders, then more blue jays will come than cardinals.”

Null: “If either sunflower seeds or millet are placed in the bird, equal numbers of cardinals and blue jays will come.”

Through simple observation, you actually find that cardinals come as often as blue jays when sunflower seeds or millet is in the bird feeder. In this case, you would reject your “if-then” hypothesis and “fail to reject” your null hypothesis . You cannot accept your first hypothesis, because it’s clearly not true. Instead you found that there was actually no relation between your different variables. Consequently, you would need to run more experiments with different variables to see if the new variables impact the results.

Experiment #4: In-Class Survey (Including an Alternative Hypothesis)

You’re about to give a speech in one of your classes about the importance of paying attention. You want to take this opportunity to test a hypothesis you’ve had for a while: 

If-then: If students sit in the first two rows of the classroom, then they will listen better than students who do not.

Null: If students sit in the first two rows of the classroom, then they will not listen better or worse than students who do not.

You give your speech and then ask your teacher if you can hand out a short survey to the class. On the survey, you’ve included questions about some of the topics you talked about. When you get back the results, you’re surprised to see that not only do the students in the first two rows not pay better attention, but they also scored worse than students in other parts of the classroom! Here, both your if-then and your null hypotheses are not representative of your findings. What do you do?

This is when you reject both your if-then and null hypotheses and instead create an alternative hypothesis . This type of hypothesis is used in the rare circumstance that neither of your hypotheses is able to capture your findings . Now you can use what you’ve learned to draft new hypotheses and test again! 

Key Takeaways: Hypothesis Writing

The more comfortable you become with writing hypotheses, the better they will become. The structure of hypotheses is flexible and may need to be changed depending on what topic you are studying. The most important thing to remember is the purpose of your hypothesis and the difference between the if-then and the null . From there, in forming your hypothesis, you should constantly be asking questions, making observations, doing secondary research, and considering your variables. After you have written your hypothesis, be sure to edit it so that it is plausible, clearly defined, observable, and helpful in explaining a general phenomenon.

Writing a hypothesis is something that everyone, from elementary school children competing in a science fair to professional scientists in a lab, needs to know how to do. Hypotheses are vital in experiments and in properly executing the scientific method . When done correctly, hypotheses will set up your studies for success and help you to understand the world a little better, one experiment at a time.

What’s Next?

If you’re studying for the science portion of the ACT, there’s definitely a lot you need to know. We’ve got the tools to help, though! Start by checking out our ultimate study guide for the ACT Science subject test. Once you read through that, be sure to download our recommended ACT Science practice tests , since they’re one of the most foolproof ways to improve your score. (And don’t forget to check out our expert guide book , too.)

If you love science and want to major in a scientific field, you should start preparing in high school . Here are the science classes you should take to set yourself up for success.

If you’re trying to think of science experiments you can do for class (or for a science fair!), here’s a list of 37 awesome science experiments you can do at home

Ashley Sufflé Robinson has a Ph.D. in 19th Century English Literature. As a content writer for PrepScholar, Ashley is passionate about giving college-bound students the in-depth information they need to get into the school of their dreams.

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How to Write a Hypothesis? Types and Examples 

All research studies involve the use of the scientific method, which is a mathematical and experimental technique used to conduct experiments by developing and testing a hypothesis or a prediction about an outcome. Simply put, a hypothesis is a suggested solution to a problem. It includes elements that are expressed in terms of relationships with each other to explain a condition or an assumption that hasn’t been verified using facts. 1 The typical steps in a scientific method include developing such a hypothesis, testing it through various methods, and then modifying it based on the outcomes of the experiments.  

A research hypothesis can be defined as a specific, testable prediction about the anticipated results of a study. 2 Hypotheses help guide the research process and supplement the aim of the study. After several rounds of testing, hypotheses can help develop scientific theories. 3 Hypotheses are often written as if-then statements. 

Here are two hypothesis examples: 

Dandelions growing in nitrogen-rich soils for two weeks develop larger leaves than those in nitrogen-poor soils because nitrogen stimulates vegetative growth. 4  

If a company offers flexible work hours, then their employees will be happier at work. 5  

Table of Contents

• What is a hypothesis? 
• Types of hypotheses 
• Characteristics of a hypothesis 
• Functions of a hypothesis 
• How to write a hypothesis 
• Hypothesis examples 
• Frequently asked questions 

What is a hypothesis?

A hypothesis expresses an expected relationship between variables in a study and is developed before conducting any research. Hypotheses are not opinions but rather are expected relationships based on facts and observations. They help support scientific research and expand existing knowledge. An incorrectly formulated hypothesis can affect the entire experiment leading to errors in the results so it’s important to know how to formulate a hypothesis and develop it carefully.

A few sources of a hypothesis include observations from prior studies, current research and experiences, competitors, scientific theories, and general conditions that can influence people. Figure 1 depicts the different steps in a research design and shows where exactly in the process a hypothesis is developed. 4  

There are seven different types of hypotheses—simple, complex, directional, nondirectional, associative and causal, null, and alternative. 

Types of hypotheses

The seven types of hypotheses are listed below: 5 , 6,7  

• Simple : Predicts the relationship between a single dependent variable and a single independent variable. 

Example: Exercising in the morning every day will increase your productivity.  

• Complex : Predicts the relationship between two or more variables. 

Example: Spending three hours or more on social media daily will negatively affect children’s mental health and productivity, more than that of adults.  

• Directional : Specifies the expected direction to be followed and uses terms like increase, decrease, positive, negative, more, or less. 

Example: The inclusion of intervention X decreases infant mortality compared to the original treatment.  

• Non-directional : Does not predict the exact direction, nature, or magnitude of the relationship between two variables but rather states the existence of a relationship. This hypothesis may be used when there is no underlying theory or if findings contradict prior research. 

Example: Cats and dogs differ in the amount of affection they express.  

• Associative and causal : An associative hypothesis suggests an interdependency between variables, that is, how a change in one variable changes the other.  

Example: There is a positive association between physical activity levels and overall health.  

A causal hypothesis, on the other hand, expresses a cause-and-effect association between variables. 

Example: Long-term alcohol use causes liver damage.  

• Null : Claims that the original hypothesis is false by showing that there is no relationship between the variables. 

Example: Sleep duration does not have any effect on productivity.  

• Alternative : States the opposite of the null hypothesis, that is, a relationship exists between two variables. 

Example: Sleep duration affects productivity.  

Characteristics of a hypothesis

So, what makes a good hypothesis? Here are some important characteristics of a hypothesis. 8,9  

• Testable : You must be able to test the hypothesis using scientific methods to either accept or reject the prediction. 
• Falsifiable : It should be possible to collect data that reject rather than support the hypothesis. 
• Logical : Hypotheses shouldn’t be a random guess but rather should be based on previous theories, observations, prior research, and logical reasoning. 
• Positive : The hypothesis statement about the existence of an association should be positive, that is, it should not suggest that an association does not exist. Therefore, the language used and knowing how to phrase a hypothesis is very important. 
• Clear and accurate : The language used should be easily comprehensible and use correct terminology. 
• Relevant : The hypothesis should be relevant and specific to the research question. 
• Structure : Should include all the elements that make a good hypothesis: variables, relationship, and outcome. 

Functions of a hypothesis

The following list mentions some important functions of a hypothesis: 1  

• Maintains the direction and progress of the research. 
• Expresses the important assumptions underlying the proposition in a single statement. 
• Establishes a suitable context for researchers to begin their investigation and for readers who are referring to the final report. 
• Provides an explanation for the occurrence of a specific phenomenon. 
• Ensures selection of appropriate and accurate facts necessary and relevant to the research subject. 

To summarize, a hypothesis provides the conceptual elements that complete the known data, conceptual relationships that systematize unordered elements, and conceptual meanings and interpretations that explain the unknown phenomena. 1  

How to write a hypothesis

Listed below are the main steps explaining how to write a hypothesis. 2,4,5  

• Make an observation and identify variables : Observe the subject in question and try to recognize a pattern or a relationship between the variables involved. This step provides essential background information to begin your research.  

For example, if you notice that an office’s vending machine frequently runs out of a specific snack, you may predict that more people in the office choose that snack over another. 

• Identify the main research question : After identifying a subject and recognizing a pattern, the next step is to ask a question that your hypothesis will answer.  

For example, after observing employees’ break times at work, you could ask “why do more employees take breaks in the morning rather than in the afternoon?” 

• Conduct some preliminary research to ensure originality and novelty : Your initial answer, which is your hypothesis, to the question is based on some pre-existing information about the subject. However, to ensure that your hypothesis has not been asked before or that it has been asked but rejected by other researchers you would need to gather additional information.  

For example, based on your observations you might state a hypothesis that employees work more efficiently when the air conditioning in the office is set at a lower temperature. However, during your preliminary research you find that this hypothesis was proven incorrect by a prior study. 

• Develop a general statement : After your preliminary research has confirmed the originality of your proposed answer, draft a general statement that includes all variables, subjects, and predicted outcome. The statement could be if/then or declarative.  
• Finalize the hypothesis statement : Use the PICOT model, which clarifies how to word a hypothesis effectively, when finalizing the statement. This model lists the important components required to write a hypothesis. 

P opulation: The specific group or individual who is the main subject of the research 

I nterest: The main concern of the study/research question 

C omparison: The main alternative group 

O utcome: The expected results  

T ime: Duration of the experiment 

Once you’ve finalized your hypothesis statement you would need to conduct experiments to test whether the hypothesis is true or false. 

Hypothesis examples

The following table provides examples of different types of hypotheses. 10 ,11  

Key takeaways  

Here’s a summary of all the key points discussed in this article about how to write a hypothesis. 

• A hypothesis is an assumption about an association between variables made based on limited evidence, which should be tested. 
• A hypothesis has four parts—the research question, independent variable, dependent variable, and the proposed relationship between the variables.   
• The statement should be clear, concise, testable, logical, and falsifiable. 
• There are seven types of hypotheses—simple, complex, directional, non-directional, associative and causal, null, and alternative. 
• A hypothesis provides a focus and direction for the research to progress. 
• A hypothesis plays an important role in the scientific method by helping to create an appropriate experimental design. 

Frequently asked questions

Hypotheses and research questions have different objectives and structure. The following table lists some major differences between the two. 9  

Here are a few examples to differentiate between a research question and hypothesis. 

Yes, here’s a simple checklist to help you gauge the effectiveness of your hypothesis. 9   1. When writing a hypothesis statement, check if it:  2. Predicts the relationship between the stated variables and the expected outcome.  3. Uses simple and concise language and is not wordy.  4. Does not assume readers’ knowledge about the subject.  5. Has observable, falsifiable, and testable results. 

As mentioned earlier in this article, a hypothesis is an assumption or prediction about an association between variables based on observations and simple evidence. These statements are usually generic. Research objectives, on the other hand, are more specific and dictated by hypotheses. The same hypothesis can be tested using different methods and the research objectives could be different in each case.     For example, Louis Pasteur observed that food lasts longer at higher altitudes, reasoned that it could be because the air at higher altitudes is cleaner (with fewer or no germs), and tested the hypothesis by exposing food to air cleaned in the laboratory. 12 Thus, a hypothesis is predictive—if the reasoning is correct, X will lead to Y—and research objectives are developed to test these predictions. 

Null hypothesis testing is a method to decide between two assumptions or predictions between variables (null and alternative hypotheses) in a statistical relationship in a sample. The null hypothesis, denoted as H 0 , claims that no relationship exists between variables in a population and any relationship in the sample reflects a sampling error or occurrence by chance. The alternative hypothesis, denoted as H 1 , claims that there is a relationship in the population. In every study, researchers need to decide whether the relationship in a sample occurred by chance or reflects a relationship in the population. This is done by hypothesis testing using the following steps: 13   1. Assume that the null hypothesis is true.  2. Determine how likely the sample relationship would be if the null hypothesis were true. This probability is called the p value.  3. If the sample relationship would be extremely unlikely, reject the null hypothesis and accept the alternative hypothesis. If the relationship would not be unlikely, accept the null hypothesis. 

To summarize, researchers should know how to write a good hypothesis to ensure that their research progresses in the required direction. A hypothesis is a testable prediction about any behavior or relationship between variables, usually based on facts and observation, and states an expected outcome.  

We hope this article has provided you with essential insight into the different types of hypotheses and their functions so that you can use them appropriately in your next research project. 

References  

• Dalen, DVV. The function of hypotheses in research. Proquest website. Accessed April 8, 2024. https://www.proquest.com/docview/1437933010?pq-origsite=gscholar&fromopenview=true&sourcetype=Scholarly%20Journals&imgSeq=1  
• McLeod S. Research hypothesis in psychology: Types & examples. SimplyPsychology website. Updated December 13, 2023. Accessed April 9, 2024. https://www.simplypsychology.org/what-is-a-hypotheses.html  
• Scientific method. Britannica website. Updated March 14, 2024. Accessed April 9, 2024. https://www.britannica.com/science/scientific-method  
• The hypothesis in science writing. Accessed April 10, 2024. https://berks.psu.edu/sites/berks/files/campus/HypothesisHandout_Final.pdf  
• How to develop a hypothesis (with elements, types, and examples). Indeed.com website. Updated February 3, 2023. Accessed April 10, 2024. https://www.indeed.com/career-advice/career-development/how-to-write-a-hypothesis  
• Types of research hypotheses. Excelsior online writing lab. Accessed April 11, 2024. https://owl.excelsior.edu/research/research-hypotheses/types-of-research-hypotheses/  
• What is a research hypothesis: how to write it, types, and examples. Researcher.life website. Published February 8, 2023. Accessed April 11, 2024. https://researcher.life/blog/article/how-to-write-a-research-hypothesis-definition-types-examples/  
• Developing a hypothesis. Pressbooks website. Accessed April 12, 2024. https://opentext.wsu.edu/carriecuttler/chapter/developing-a-hypothesis/  
• What is and how to write a good hypothesis in research. Elsevier author services website. Accessed April 12, 2024. https://scientific-publishing.webshop.elsevier.com/manuscript-preparation/what-how-write-good-hypothesis-research/  
• How to write a great hypothesis. Verywellmind website. Updated March 12, 2023. Accessed April 13, 2024. https://www.verywellmind.com/what-is-a-hypothesis-2795239  
• 15 Hypothesis examples. Helpfulprofessor.com Published September 8, 2023. Accessed March 14, 2024. https://helpfulprofessor.com/hypothesis-examples/ 
• Editage insights. What is the interconnectivity between research objectives and hypothesis? Published February 24, 2021. Accessed April 13, 2024. https://www.editage.com/insights/what-is-the-interconnectivity-between-research-objectives-and-hypothesis  
• Understanding null hypothesis testing. BCCampus open publishing. Accessed April 16, 2024. https://opentextbc.ca/researchmethods/chapter/understanding-null-hypothesis-testing/#:~:text=In%20null%20hypothesis%20testing%2C%20this,said%20to%20be%20statistically%20significant  

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What is Hypothesis?

We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.

A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

• The hypothesis should be clear and precise to consider it to be reliable.
• If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables.
• The hypothesis must be specific and should have scope for conducting more tests.
• The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance.

Sources of Hypothesis

Following are the sources of hypothesis:

• The resemblance between the phenomenon.
• Observations from past studies, present-day experiences and from the competitors.
• Scientific theories.
• General patterns that influence the thinking process of people.

Types of Hypothesis

There are six forms of hypothesis and they are:

• Simple hypothesis
• Complex hypothesis
• Directional hypothesis
• Non-directional hypothesis
• Null hypothesis
• Associative and casual hypothesis

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Null Hypothesis

It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.

Examples of Hypothesis

Following are the examples of hypotheses based on their types:

• Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis.
• All lilies have the same number of petals is an example of a null hypothesis.
• If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis.

Functions of Hypothesis

Following are the functions performed by the hypothesis:

• Hypothesis helps in making an observation and experiments possible.
• It becomes the start point for the investigation.
• Hypothesis helps in verifying the observations.
• It helps in directing the inquiries in the right direction.

How will Hypothesis help in the Scientific Method?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Formation of question
• Doing background research
• Creation of hypothesis
• Designing an experiment
• Collection of data
• Result analysis
• Summarizing the experiment
• Communicating the results

Frequently Asked Questions – FAQs

What is hypothesis.

A hypothesis is an assumption made based on some evidence.

Give an example of simple hypothesis?

What are the types of hypothesis.

Types of hypothesis are:

• Associative and Casual hypothesis

State true or false: Hypothesis is the initial point of any investigation that translates the research questions into a prediction.

Define complex hypothesis..

A complex hypothesis shows the relationship between two or more dependent variables and two or more independent variables.

Put your understanding of this concept to test by answering a few MCQs. Click ‘Start Quiz’ to begin!

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Hypothesis testing involves formulating assumptions about population parameters based on sample statistics and rigorously evaluating these assumptions against empirical evidence. This article sheds light on the significance of hypothesis testing and the critical steps involved in the process.

What is Hypothesis Testing?

Hypothesis testing is a statistical method that is used to make a statistical decision using experimental data. Hypothesis testing is basically an assumption that we make about a population parameter. It evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data. 

Example: You say an average height in the class is 30 or a boy is taller than a girl. All of these is an assumption that we are assuming, and we need some statistical way to prove these. We need some mathematical conclusion whatever we are assuming is true.

Defining Hypotheses

Key Terms of Hypothesis Testing

• P-value: The P value , or calculated probability, is the probability of finding the observed/extreme results when the null hypothesis(H0) of a study-given problem is true. If your P-value is less than the chosen significance level then you reject the null hypothesis i.e. accept that your sample claims to support the alternative hypothesis.
• Test Statistic: The test statistic is a numerical value calculated from sample data during a hypothesis test, used to determine whether to reject the null hypothesis. It is compared to a critical value or p-value to make decisions about the statistical significance of the observed results.
• Critical value : The critical value in statistics is a threshold or cutoff point used to determine whether to reject the null hypothesis in a hypothesis test.
• Degrees of freedom: Degrees of freedom are associated with the variability or freedom one has in estimating a parameter. The degrees of freedom are related to the sample size and determine the shape.

Why do we use Hypothesis Testing?

Hypothesis testing is an important procedure in statistics. Hypothesis testing evaluates two mutually exclusive population statements to determine which statement is most supported by sample data. When we say that the findings are statistically significant, thanks to hypothesis testing. 

One-Tailed and Two-Tailed Test

One tailed test focuses on one direction, either greater than or less than a specified value. We use a one-tailed test when there is a clear directional expectation based on prior knowledge or theory. The critical region is located on only one side of the distribution curve. If the sample falls into this critical region, the null hypothesis is rejected in favor of the alternative hypothesis.

One-Tailed Test

There are two types of one-tailed test:

Two-Tailed Test

A two-tailed test considers both directions, greater than and less than a specified value.We use a two-tailed test when there is no specific directional expectation, and want to detect any significant difference.

What are Type 1 and Type 2 errors in Hypothesis Testing?

In hypothesis testing, Type I and Type II errors are two possible errors that researchers can make when drawing conclusions about a population based on a sample of data. These errors are associated with the decisions made regarding the null hypothesis and the alternative hypothesis.

How does Hypothesis Testing work?

Step 1: define null and alternative hypothesis.

We first identify the problem about which we want to make an assumption keeping in mind that our assumption should be contradictory to one another, assuming Normally distributed data.

Step 2 – Choose significance level

Step 3 – Collect and Analyze data.

Gather relevant data through observation or experimentation. Analyze the data using appropriate statistical methods to obtain a test statistic.

Step 4-Calculate Test Statistic

The data for the tests are evaluated in this step we look for various scores based on the characteristics of data. The choice of the test statistic depends on the type of hypothesis test being conducted.

There are various hypothesis tests, each appropriate for various goal to calculate our test. This could be a Z-test , Chi-square , T-test , and so on.

• Z-test : If population means and standard deviations are known. Z-statistic is commonly used.
• t-test : If population standard deviations are unknown. and sample size is small than t-test statistic is more appropriate.
• Chi-square test : Chi-square test is used for categorical data or for testing independence in contingency tables
• F-test : F-test is often used in analysis of variance (ANOVA) to compare variances or test the equality of means across multiple groups.

We have a smaller dataset, So, T-test is more appropriate to test our hypothesis.

T-statistic is a measure of the difference between the means of two groups relative to the variability within each group. It is calculated as the difference between the sample means divided by the standard error of the difference. It is also known as the t-value or t-score.

Step 5 – Comparing Test Statistic:

In this stage, we decide where we should accept the null hypothesis or reject the null hypothesis. There are two ways to decide where we should accept or reject the null hypothesis.

Method A: Using Crtical values

Comparing the test statistic and tabulated critical value we have,

• If Test Statistic>Critical Value: Reject the null hypothesis.
• If Test Statistic≤Critical Value: Fail to reject the null hypothesis.

Note: Critical values are predetermined threshold values that are used to make a decision in hypothesis testing. To determine critical values for hypothesis testing, we typically refer to a statistical distribution table , such as the normal distribution or t-distribution tables based on.

Method B: Using P-values

We can also come to an conclusion using the p-value,

Note : The p-value is the probability of obtaining a test statistic as extreme as, or more extreme than, the one observed in the sample, assuming the null hypothesis is true. To determine p-value for hypothesis testing, we typically refer to a statistical distribution table , such as the normal distribution or t-distribution tables based on.

Step 7- Interpret the Results

At last, we can conclude our experiment using method A or B.

Calculating test statistic

To validate our hypothesis about a population parameter we use statistical functions . We use the z-score, p-value, and level of significance(alpha) to make evidence for our hypothesis for normally distributed data .

1. Z-statistics:

When population means and standard deviations are known.

• μ represents the population mean, 
• σ is the standard deviation
• and n is the size of the sample.

2. T-Statistics

T test is used when n<30,

t-statistic calculation is given by:

• t = t-score,
• x̄ = sample mean
• μ = population mean,
• s = standard deviation of the sample,
• n = sample size

3. Chi-Square Test

Chi-Square Test for Independence categorical Data (Non-normally distributed) using:

• i,j are the rows and columns index respectively.

Real life Hypothesis Testing example

Let’s examine hypothesis testing using two real life situations,

Case A: D oes a New Drug Affect Blood Pressure?

Imagine a pharmaceutical company has developed a new drug that they believe can effectively lower blood pressure in patients with hypertension. Before bringing the drug to market, they need to conduct a study to assess its impact on blood pressure.

• Before Treatment: 120, 122, 118, 130, 125, 128, 115, 121, 123, 119
• After Treatment: 115, 120, 112, 128, 122, 125, 110, 117, 119, 114

Step 1 : Define the Hypothesis

• Null Hypothesis : (H 0 )The new drug has no effect on blood pressure.
• Alternate Hypothesis : (H 1 )The new drug has an effect on blood pressure.

Step 2: Define the Significance level

Let’s consider the Significance level at 0.05, indicating rejection of the null hypothesis.

If the evidence suggests less than a 5% chance of observing the results due to random variation.

Step 3 : Compute the test statistic

Using paired T-test analyze the data to obtain a test statistic and a p-value.

The test statistic (e.g., T-statistic) is calculated based on the differences between blood pressure measurements before and after treatment.

t = m/(s/√n)

• m  = mean of the difference i.e X after, X before
• s  = standard deviation of the difference (d) i.e d i ​= X after, i ​− X before,
• n  = sample size,

then, m= -3.9, s= 1.8 and n= 10

we, calculate the , T-statistic = -9 based on the formula for paired t test

Step 4: Find the p-value

The calculated t-statistic is -9 and degrees of freedom df = 9, you can find the p-value using statistical software or a t-distribution table.

thus, p-value = 8.538051223166285e-06

Step 5: Result

• If the p-value is less than or equal to 0.05, the researchers reject the null hypothesis.
• If the p-value is greater than 0.05, they fail to reject the null hypothesis.

Conclusion: Since the p-value (8.538051223166285e-06) is less than the significance level (0.05), the researchers reject the null hypothesis. There is statistically significant evidence that the average blood pressure before and after treatment with the new drug is different.

Python Implementation of Hypothesis Testing

Let’s create hypothesis testing with python, where we are testing whether a new drug affects blood pressure. For this example, we will use a paired T-test. We’ll use the scipy.stats library for the T-test.

Scipy is a mathematical library in Python that is mostly used for mathematical equations and computations.

We will implement our first real life problem via python,

In the above example, given the T-statistic of approximately -9 and an extremely small p-value, the results indicate a strong case to reject the null hypothesis at a significance level of 0.05. 

• The results suggest that the new drug, treatment, or intervention has a significant effect on lowering blood pressure.
• The negative T-statistic indicates that the mean blood pressure after treatment is significantly lower than the assumed population mean before treatment.

Case B : Cholesterol level in a population

Data: A sample of 25 individuals is taken, and their cholesterol levels are measured.

Cholesterol Levels (mg/dL): 205, 198, 210, 190, 215, 205, 200, 192, 198, 205, 198, 202, 208, 200, 205, 198, 205, 210, 192, 205, 198, 205, 210, 192, 205.

Populations Mean = 200

Population Standard Deviation (σ): 5 mg/dL(given for this problem)

Step 1: Define the Hypothesis

• Null Hypothesis (H 0 ): The average cholesterol level in a population is 200 mg/dL.
• Alternate Hypothesis (H 1 ): The average cholesterol level in a population is different from 200 mg/dL.

As the direction of deviation is not given , we assume a two-tailed test, and based on a normal distribution table, the critical values for a significance level of 0.05 (two-tailed) can be calculated through the z-table and are approximately -1.96 and 1.96.

Step 4: Result

Since the absolute value of the test statistic (2.04) is greater than the critical value (1.96), we reject the null hypothesis. And conclude that, there is statistically significant evidence that the average cholesterol level in the population is different from 200 mg/dL

Limitations of Hypothesis Testing

• Although a useful technique, hypothesis testing does not offer a comprehensive grasp of the topic being studied. Without fully reflecting the intricacy or whole context of the phenomena, it concentrates on certain hypotheses and statistical significance.
• The accuracy of hypothesis testing results is contingent on the quality of available data and the appropriateness of statistical methods used. Inaccurate data or poorly formulated hypotheses can lead to incorrect conclusions.
• Relying solely on hypothesis testing may cause analysts to overlook significant patterns or relationships in the data that are not captured by the specific hypotheses being tested. This limitation underscores the importance of complimenting hypothesis testing with other analytical approaches.

Hypothesis testing stands as a cornerstone in statistical analysis, enabling data scientists to navigate uncertainties and draw credible inferences from sample data. By systematically defining null and alternative hypotheses, choosing significance levels, and leveraging statistical tests, researchers can assess the validity of their assumptions. The article also elucidates the critical distinction between Type I and Type II errors, providing a comprehensive understanding of the nuanced decision-making process inherent in hypothesis testing. The real-life example of testing a new drug’s effect on blood pressure using a paired T-test showcases the practical application of these principles, underscoring the importance of statistical rigor in data-driven decision-making.

Frequently Asked Questions (FAQs)

1. what are the 3 types of hypothesis test.

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed. Right-tailed tests assess if a parameter is greater, left-tailed if lesser. Two-tailed tests check for non-directional differences, greater or lesser.

2.What are the 4 components of hypothesis testing?

Null Hypothesis ( ): No effect or difference exists. Alternative Hypothesis ( ): An effect or difference exists. Significance Level ( ): Risk of rejecting null hypothesis when it’s true (Type I error). Test Statistic: Numerical value representing observed evidence against null hypothesis.

3.What is hypothesis testing in ML?

Statistical method to evaluate the performance and validity of machine learning models. Tests specific hypotheses about model behavior, like whether features influence predictions or if a model generalizes well to unseen data.

4.What is the difference between Pytest and hypothesis in Python?

Pytest purposes general testing framework for Python code while Hypothesis is a Property-based testing framework for Python, focusing on generating test cases based on specified properties of the code.

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C3.ai is undervalued, but will face profitability pressures.

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The company has no debt but a growing net loss, while Palantir is the stronger peer

By Oliver Rodzianko

• C3.ai is operationally undervalued compared to Palantir and holds potential for good long-term returns, albeit likely not exceeding those of its competitor.
• Despite not being profitable yet, C3.ai’s balance sheet suggests stable financial management, but there needs to be more effective cost controls to achieve profitability.
• C3.ai may face changes in resource allocation if defense and intelligence demand increase during periods of heightened global conflict. In turn, this could affect shareholder returns.

C3.ai Inc. ( AI , Financial ) is operationally undervalued by the market, primarily because it has stronger competitors like Palantir Technologies Inc. ( PLTR , Financial ). I believe investors will have reasonable long-term returns if they invest in the stock now, but I do not expect these to be higher than Palantir's.

As such, I believe investors will want to consider the similar risk-reward profile of both companies and then assess the higher growth prospects of Palantir. Because both investments carry somewhat higher risk than other established players, my own preference is to choose the allocation that may provide a significant amount of outsized growth as a result.

Company and AI market analysis

C3.ai is one of the lesser-known but still important companies working in the artificial intelligence industry. It specializes in enterprise AI, with notable competitors in the space, including Salesforce ( CRM , Financial ) and Palantir. Its flagship product, the C3 AI Suite, offers the development, deployment and operation of AI applications, driving efficiency and cost-effectiveness with a focus on enterprise data management.

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The company offers a crucial product and service set, but I am unsure how unique its offering is. Hence, I believe it may fall prey to finding itself both easily replicated and potentially outcompeted by more established and profitable players, but also by the newer companies that potentially have some advantage already in capability, like Palantir. C3.ai's strategic partnerships are strong and include Microsoft ( MSFT , Financial ) and Adobe ( ADBE , Financial ), but these partnerships are becoming increasingly common for enterprise AI companies and are nothing particularly special, in my opinion.

That being said, the executive leadership team is formidable, with Chairman and CEO Thomas M. Siebel having founded Siebel Systems, which merged with Oracle ( ORCL , Financial ) in 2006. That extensive history in application software sets the stage with a veteran-level consciousness in applying time-tested lessons to these new burgeoning AI tools. I do not underestimate Siebel's experience and accolades, but I wonder how he will keep C3.ai competitive at a time when AI is literally the most popular business sector in the world. It seems probable to me that even strong companies, which are large and have hierarchies of executives, each with decades of technology knowledge, could get overpowered by other companies that simply get lucky in mass-scale adoption. For example, Salesforce is a company that seems to have quite crucially cornered the retail AI market early enough to potentially solidify itself as the main provider. Even if C3.ai can provide tools that are better and more comprehensive than Salesforce, the idea that it will get back the portion of the market that Salesforce seems to have already captured appears thin to me.

There is also Palantir, which I believe will outcompete C3.ai in U.S. defense work. C3.ai has contracts with the U.S. Department of Defense, the Defense Innovation Unit and the U.S. Air Force, and it is part of the Joint Artificial Intelligence Center project. This raises concern over how these AI technologies will be used and whether C3.ai will be pulled in directions that perhaps conflict with its corporate customers for the sake of national and international defense during wartime.

My own perspective is that even in the case of C3.ai favoring its government defense obligations to its corporate interests, Palantir offers the management of complexity, security and compliance, which outcompetes C3.ai in the defense sector at this time. Palantir was founded six years earlier, so I believe it has some advantage in operations simply due to time in the field.

Financial analysis

C3.ai is not profitable yet, and I believe this provides investors with somewhat of an opportunity because it is usually when companies begin to report stable earnings that the wider stock market begins to take notice and push up the company's valuation. However, it also comes with a set of risks that make me skeptical about allocating significantly to the stock.

In many respects, the capital structure has to be admired because the company carries no typical debt, although it does have moderate lease obligations and other liabilities contributing to its equity-to-asset ratio of 0.84. This is high, and I believe it shows strong financial management that should translate into a stable bottom line in due course. Additionally, the company's operations with a portfolio of high-status clients, including government contracts, show signs of potentially stable future earnings. The good news is that C3.ai's revenue is growing at a healthy rate and it is quite understandable that it is investing heavily in research and development and its operating costs, as well as its cost of goods sold, are increasing quickly alongside its revenue. However, I believe C3.ai needs to take more stringent efficiency measures to create lasting profitability. What does not help is that C3.ai's gross margin of 59% has been decreasing, while Palantir's 81% margin has been increasing. The unfortunate reality right now is that while C3.ai's revenue is increasing fast, so is its net loss.

Changes in C3.ai's anual revenue and net income.

C3.ai has a roughly $2.50 billion market cap, while Palantir has a $47 billion market cap. Therefore, it is not unreasonable to say that C3.ai has a lot of catching up to do. Considering the problem is as immediate as the gross margin for C3.ai, it likely has higher purchase costs for product and service development because it is purchasing at a lower scale. In addition, it does not have the same full-stack AI and data services that Palantir has, likely driving up its cost of goods sold due to fewer in-house development capabilities.

Value analysis

Based on my analysis, it is not surprising that Palantir trades at higher valuation multiples than C3.ai. We can see evidence here of the efficient market hypothesis at work. To clarify, my own position is the markets are efficient, but evidently not totally efficient. I also have seen evidence that the smaller the company, the larger the inefficiency is likely to be if it occurs.

C3.ai is a mid-cap stock, while Palantir is a large-cap stock, so any value opportunities here are unlikely to be significant. However, C3.ai undoubtedly offers a better valuation to Palantir at this time, primarily a result of the fact that investors at large have begun to notice the long-term high value of Palantir as a U.S. defense and corporate asset and a company vital in harnessing AI for data management. I believe the market operationally undervalues C3.ai, and so its price reflects this. Consider its price-sales ratio of 8.13 versus Palantir's price-sales ratio of 21.90. As I mentioned above, the market's lower favor of C3.ai is well justified by Palantir's more comprehensive capabilities and financing power.

Additionally, Palantir offers better future growth prospects assessed on consensus by analysts. Therefore, I do not believe the most astute question here when analyzing the valuation of C3.ai or Palantir is whether they are selling below fair value, but rather whether the valuation is reasonable enough to justify the growth. In my opinion, Palantir offers much better future growth prospects and operational value, so the premium is worth it. I admit that Palantir could be selling slightly cheaper and I believe it will see a small correction soon. But even if bought at this time, I believe Palantir is likely to significantly outperform C3.ai over the next 10 years.

Risk analysis

As mentioned previously, C3.ai faces one of the same significant risks that I recently identified when researching Palantir. As it has a growing exposure to defense, intelligence and government sectors, it may find that during wartime, its resource allocation is conflicted between corporate interests and national and international security measures. It is my opinion that with the current global conflicts, which could escalate, C3.ai may be called upon with larger contracts and more definite demands from U.S. and NATO defense allies.

As such, it might find its development costs significantly redirected from corporate to defense interests for extended periods, significantly reducing its value to corporate clients during these periods and affecting or changing its reputation on return to times of peace. C3.ai's exposure to the military-industrial complex places shareholders in a nuanced position, in my opinion, and opens it up to certain risks related to shifts in its strategic focus that may not necessarily always be what is best for shareholder profits.

C3.ai is well-positioned to provide AI services to enterprises looking to become more efficient and automated, but it has fierce competition, most notably from Palantir. While its balance sheet is very strong, its net loss is growing alongside its revenue growth. Additionally, in times of war, C3.ai could find its operational focus significantly changes, affecting its utility to corporate clients and associated value when returning to times of peace.

Although the stock offers good value at this time, I believe investors are more likely to have higher total returns from an investment in Palantir over the long term.

Disclosures

I am/we currently own positions in the stocks mentioned, and have NO plans to sell some or all of the positions in the stocks mentioned over the next 72 hours.

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