introduction essay for scientific method

Scientific Method: Role and Importance Essay

The scientific method is a problem-solving strategy that is at the heart of biology and other sciences. There are five steps included in the scientific method that is making an observation, asking a question, forming a hypothesis or an explanation that could be tested, and predicting the test. After that, in the feedback step that is iterating, the results are used to make new predictions. The scientific method is almost always an iterative process. In other words, rather than a straight line, it is a cycle. The outcome of one round of questioning generates feedback that helps to enhance the next round of questioning.

Science is an activity that involves the logical explanation, prediction, and control of empirical phenomena. The concepts of reasoning applicable to the pursuit of this endeavor are referred to as scientific reasoning (Cowles, 2020). They include topics such as experimental design, hypothesis testing, and data interpretation. All sciences, including social sciences, follow the scientific method (Cowles, 2020). Different questions and tests are asked and performed by scientists in various domains. They do, however, have a common approach to finding logical and evidence-based answers.

Scientific reasoning is fundamental for all types of scientific study, not simply institutional research. Scientists do employ specific ideas that non-scientists do not have to use in everyday life. However, many reasoning principles are useful in everyday life. Even if one is not a scientist, they must use excellent reasoning to understand, anticipate, and regulate the events that occur in the environment. When one wants to start their careers, preserve their finances, or enhance their health, they need to acquire evidence to determine the most effective method for achieving our goals. Good scientific thinking skills come in handy in all of these situations.

Experiments, surveys, case studies, descriptive studies, and non-descriptive studies are all forms of research used in the scientific method. In an experiment, a researcher manipulates certain factors in a controlled environment and assesses their impact on other variables (Black, 2018). Descriptive research focuses on the nature of the relationship between the variables being studied rather than on cause and effect. A case study is a detailed examination of a single instance in which something unexpected has occurred. This is normally done with a single individual in extreme or exceptional instances. Large groups of individuals are polled to answer questions about certain topics in surveys. Correlational approaches are used in non-descriptive investigations to anticipate the link between two or more variables.

The Lau and Chan technique describes how to assess the validity of a theory or hypothesis using the scientific method, also known as the hypothetical-deductive method (Lau & Chan, 2017). For testing theories or hypotheses, the hypothetical-deductive technique (HD method) is highly useful. It is sometimes referred to as “scientific procedure.” This is not quite right because science can’t possibly employ only one approach. However, the HD technique is critical since it is one of the most fundamental approaches used in many scientific disciplines, including economics, physics, and biochemistry. Its implementation can be broken down into four stages. The stages include using the hypothetical-deductive method, identifying the testable hypothesis, generating the predictions according to the hypothesis, and using experiments in order to check the predictions (Cowles, 2020). If the predictions that are tested turn out to be correct, the hypothesis will be confirmed. Suppose the results are incorrect; the hypothesis would be disconfirmed.

The HD method instructs us on how to test a hypothesis, and each scientific theory must be testable.

One cannot discover evidence to illustrate whether a theory is likely or not if it cannot be tested. It cannot be considered scientific information in that circumstance. Consider the possibility that there are ghosts that people cannot see, cannot communicate with, and cannot be detected directly or indirectly. This hypothesis is defined in such a way that testing is not possible. It could still be real, and there could be such ghosts, but people would never know; thus, this cannot be considered a scientific hypothesis. In general, validating a theory’s predictions raises the likelihood that it is right. However, this does not establish definitively that the theory is right in and of itself. When given additional assumptions, a hypothesis frequently creates a prediction. When a forecast fails in this way, the theory may still be valid.

When a theory makes a faulty prediction, it might be difficult to determine whether the theory should be rejected or whether the auxiliary assumptions are flawed. Astronomers in the 19th century, for example, discovered that Newtonian physics could not adequately explain the orbit of the planet Mercury. This is due to the fact that Newtonian physics is incorrect, and you require relativity to get a more accurate orbit prediction. When astronomers discovered Uranus in 1781, they discovered that its orbit did not match Newtonian physics predictions. However, astronomers concluded that it could be explained if Uranus was being affected by another planet, and Neptune was discovered as a result.

I had several instances where I have made assumptions on an important issue regardless of evidence. Once I have prepared the work on the topic of power distribution in the workplace and its relation to gender, I have assumed that possibly because of the general feminine traits, women are less likely to create a strong image of power in comparison with men. In fact, such a hypothesis needs to be tested, and it is testable. For example, I could first define what is meant by feminine traits by collecting data from different biological and psychological sources. After that, I could observe the information regarding what factors or behavior patterns contribute to establishing power in the workplace. If I found the correlation between the feminine character traits, communication style, and behavioral patterns with the distribution of power in the workplace, then I could confirm my hypothesis.

Thus, applying the scientific method can help to improve critical reasoning by using tools from scientific reasoning. By supporting the provided hypothesis with evidence from scientific research and statistical data, one can make their claim more valuable and objective. The scientific method is essential for the creation of scientific theories that explain information and ideas in a scientifically rational manner. In a typical scientific method application, a researcher makes a hypothesis, tests it using various methods, and then alters it based on the results of the tests and experiments. The new hypothesis is then retested, further changed, and retested until it matches observable events and testing results. Hypotheses serve as tools for scientists to collect data in this way. Scientists can build broad general explanations, or scientific theories, based on that evidence and the numerous scientific experiments conducted to investigate possibilities. In conclusion, a scientific method is an important approach to examining the hypothesis. By using the tools of the scientific method, the inferences become rational and objective.

Black, M. (2018). Critical thinking: An introduction to logic and scientific method . Pickle Partners Publishing.

Cowles, H. M. (2020). The Scientific Method . Harvard University Press.

Lau, J., & Chan, J. (2017). Scientific methodology: Tutorials 1-9 .

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Writing an Introduction for a Scientific Paper

Dr. michelle harris, dr. janet batzli, biocore.

This section provides guidelines on how to construct a solid introduction to a scientific paper including background information, study question , biological rationale, hypothesis , and general approach . If the Introduction is done well, there should be no question in the reader’s mind why and on what basis you have posed a specific hypothesis.

Broad Question : based on an initial observation (e.g., “I see a lot of guppies close to the shore. Do guppies like living in shallow water?”). This observation of the natural world may inspire you to investigate background literature or your observation could be based on previous research by others or your own pilot study. Broad questions are not always included in your written text, but are essential for establishing the direction of your research.

Background Information : key issues, concepts, terminology, and definitions needed to understand the biological rationale for the experiment. It often includes a summary of findings from previous, relevant studies. Remember to cite references, be concise, and only include relevant information given your audience and your experimental design. Concisely summarized background information leads to the identification of specific scientific knowledge gaps that still exist. (e.g., “No studies to date have examined whether guppies do indeed spend more time in shallow water.”)

Testable Question : these questions are much more focused than the initial broad question, are specific to the knowledge gap identified, and can be addressed with data. (e.g., “Do guppies spend different amounts of time in water <1 meter deep as compared to their time in water that is >1 meter deep?”)

Biological Rationale : describes the purpose of your experiment distilling what is known and what is not known that defines the knowledge gap that you are addressing. The “BR” provides the logic for your hypothesis and experimental approach, describing the biological mechanism and assumptions that explain why your hypothesis should be true.

The biological rationale is based on your interpretation of the scientific literature, your personal observations, and the underlying assumptions you are making about how you think the system works. If you have written your biological rationale, your reader should see your hypothesis in your introduction section and say to themselves, “Of course, this hypothesis seems very logical based on the rationale presented.”

• A thorough rationale defines your assumptions about the system that have not been revealed in scientific literature or from previous systematic observation. These assumptions drive the direction of your specific hypothesis or general predictions.
• Defining the rationale is probably the most critical task for a writer, as it tells your reader why your research is biologically meaningful. It may help to think about the rationale as an answer to the questions— how is this investigation related to what we know, what assumptions am I making about what we don’t yet know, AND how will this experiment add to our knowledge? *There may or may not be broader implications for your study; be careful not to overstate these (see note on social justifications below).
• Expect to spend time and mental effort on this. You may have to do considerable digging into the scientific literature to define how your experiment fits into what is already known and why it is relevant to pursue.
• Be open to the possibility that as you work with and think about your data, you may develop a deeper, more accurate understanding of the experimental system. You may find the original rationale needs to be revised to reflect your new, more sophisticated understanding.
• As you progress through Biocore and upper level biology courses, your rationale should become more focused and matched with the level of study e ., cellular, biochemical, or physiological mechanisms that underlie the rationale. Achieving this type of understanding takes effort, but it will lead to better communication of your science.

***Special note on avoiding social justifications: You should not overemphasize the relevance of your experiment and the possible connections to large-scale processes. Be realistic and logical —do not overgeneralize or state grand implications that are not sensible given the structure of your experimental system. Not all science is easily applied to improving the human condition. Performing an investigation just for the sake of adding to our scientific knowledge (“pure or basic science”) is just as important as applied science. In fact, basic science often provides the foundation for applied studies.

Hypothesis / Predictions : specific prediction(s) that you will test during your experiment. For manipulative experiments, the hypothesis should include the independent variable (what you manipulate), the dependent variable(s) (what you measure), the organism or system , the direction of your results, and comparison to be made.

If you are doing a systematic observation , your hypothesis presents a variable or set of variables that you predict are important for helping you characterize the system as a whole, or predict differences between components/areas of the system that help you explain how the system functions or changes over time.

Experimental Approach : Briefly gives the reader a general sense of the experiment, the type of data it will yield, and the kind of conclusions you expect to obtain from the data. Do not confuse the experimental approach with the experimental protocol . The experimental protocol consists of the detailed step-by-step procedures and techniques used during the experiment that are to be reported in the Methods and Materials section.

Some Final Tips on Writing an Introduction

• As you progress through the Biocore sequence, for instance, from organismal level of Biocore 301/302 to the cellular level in Biocore 303/304, we expect the contents of your “Introduction” paragraphs to reflect the level of your coursework and previous writing experience. For example, in Biocore 304 (Cell Biology Lab) biological rationale should draw upon assumptions we are making about cellular and biochemical processes.
• Be Concise yet Specific: Remember to be concise and only include relevant information given your audience and your experimental design. As you write, keep asking, “Is this necessary information or is this irrelevant detail?” For example, if you are writing a paper claiming that a certain compound is a competitive inhibitor to the enzyme alkaline phosphatase and acts by binding to the active site, you need to explain (briefly) Michaelis-Menton kinetics and the meaning and significance of Km and Vmax. This explanation is not necessary if you are reporting the dependence of enzyme activity on pH because you do not need to measure Km and Vmax to get an estimate of enzyme activity.
• Another example: if you are writing a paper reporting an increase in Daphnia magna heart rate upon exposure to caffeine you need not describe the reproductive cycle of magna unless it is germane to your results and discussion. Be specific and concrete, especially when making introductory or summary statements.

Where Do You Discuss Pilot Studies? Many times it is important to do pilot studies to help you get familiar with your experimental system or to improve your experimental design. If your pilot study influences your biological rationale or hypothesis, you need to describe it in your Introduction. If your pilot study simply informs the logistics or techniques, but does not influence your rationale, then the description of your pilot study belongs in the Materials and Methods section.  

How will introductions be evaluated? The following is part of the rubric we will be using to evaluate your papers.

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Biology library

Course: biology library   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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What is the Scientific Method: How does it work and why is it important?

The scientific method is a systematic process involving steps like defining questions, forming hypotheses, conducting experiments, and analyzing data. It minimizes biases and enables replicable research, leading to groundbreaking discoveries like Einstein's theory of relativity, penicillin, and the structure of DNA. This ongoing approach promotes reason, evidence, and the pursuit of truth in science.

Updated on November 18, 2023

Beginning in elementary school, we are exposed to the scientific method and taught how to put it into practice. As a tool for learning, it prepares children to think logically and use reasoning when seeking answers to questions.

Rather than jumping to conclusions, the scientific method gives us a recipe for exploring the world through observation and trial and error. We use it regularly, sometimes knowingly in academics or research, and sometimes subconsciously in our daily lives.

In this article we will refresh our memories on the particulars of the scientific method, discussing where it comes from, which elements comprise it, and how it is put into practice. Then, we will consider the importance of the scientific method, who uses it and under what circumstances.

What is the scientific method?

The scientific method is a dynamic process that involves objectively investigating questions through observation and experimentation . Applicable to all scientific disciplines, this systematic approach to answering questions is more accurately described as a flexible set of principles than as a fixed series of steps.

The following representations of the scientific method illustrate how it can be both condensed into broad categories and also expanded to reveal more and more details of the process. These graphics capture the adaptability that makes this concept universally valuable as it is relevant and accessible not only across age groups and educational levels but also within various contexts.

Steps in the scientific method

While the scientific method is versatile in form and function, it encompasses a collection of principles that create a logical progression to the process of problem solving:

• Define a question : Constructing a clear and precise problem statement that identifies the main question or goal of the investigation is the first step. The wording must lend itself to experimentation by posing a question that is both testable and measurable.
• Gather information and resources : Researching the topic in question to find out what is already known and what types of related questions others are asking is the next step in this process. This background information is vital to gaining a full understanding of the subject and in determining the best design for experiments. 
• Form a hypothesis : Composing a concise statement that identifies specific variables and potential results, which can then be tested, is a crucial step that must be completed before any experimentation. An imperfection in the composition of a hypothesis can result in weaknesses to the entire design of an experiment.
• Perform the experiments : Testing the hypothesis by performing replicable experiments and collecting resultant data is another fundamental step of the scientific method. By controlling some elements of an experiment while purposely manipulating others, cause and effect relationships are established.
• Analyze the data : Interpreting the experimental process and results by recognizing trends in the data is a necessary step for comprehending its meaning and supporting the conclusions. Drawing inferences through this systematic process lends substantive evidence for either supporting or rejecting the hypothesis.
• Report the results : Sharing the outcomes of an experiment, through an essay, presentation, graphic, or journal article, is often regarded as a final step in this process. Detailing the project's design, methods, and results not only promotes transparency and replicability but also adds to the body of knowledge for future research.
• Retest the hypothesis : Repeating experiments to see if a hypothesis holds up in all cases is a step that is manifested through varying scenarios. Sometimes a researcher immediately checks their own work or replicates it at a future time, or another researcher will repeat the experiments to further test the hypothesis.

Where did the scientific method come from?

Oftentimes, ancient peoples attempted to answer questions about the unknown by:

• Making simple observations
• Discussing the possibilities with others deemed worthy of a debate
• Drawing conclusions based on dominant opinions and preexisting beliefs

For example, take Greek and Roman mythology. Myths were used to explain everything from the seasons and stars to the sun and death itself.

However, as societies began to grow through advancements in agriculture and language, ancient civilizations like Egypt and Babylonia shifted to a more rational analysis for understanding the natural world. They increasingly employed empirical methods of observation and experimentation that would one day evolve into the scientific method . 

In the 4th century, Aristotle, considered the Father of Science by many, suggested these elements , which closely resemble the contemporary scientific method, as part of his approach for conducting science:

• Study what others have written about the subject.
• Look for the general consensus about the subject.
• Perform a systematic study of everything even partially related to the topic.

By continuing to emphasize systematic observation and controlled experiments, scholars such as Al-Kindi and Ibn al-Haytham helped expand this concept throughout the Islamic Golden Age . 

In his 1620 treatise, Novum Organum , Sir Francis Bacon codified the scientific method, arguing not only that hypotheses must be tested through experiments but also that the results must be replicated to establish a truth. Coming at the height of the Scientific Revolution, this text made the scientific method accessible to European thinkers like Galileo and Isaac Newton who then put the method into practice.

As science modernized in the 19th century, the scientific method became more formalized, leading to significant breakthroughs in fields such as evolution and germ theory. Today, it continues to evolve, underpinning scientific progress in diverse areas like quantum mechanics, genetics, and artificial intelligence.

Why is the scientific method important?

The history of the scientific method illustrates how the concept developed out of a need to find objective answers to scientific questions by overcoming biases based on fear, religion, power, and cultural norms. This still holds true today.

By implementing this standardized approach to conducting experiments, the impacts of researchers’ personal opinions and preconceived notions are minimized. The organized manner of the scientific method prevents these and other mistakes while promoting the replicability and transparency necessary for solid scientific research.

The importance of the scientific method is best observed through its successes, for example: 

• “ Albert Einstein stands out among modern physicists as the scientist who not only formulated a theory of revolutionary significance but also had the genius to reflect in a conscious and technical way on the scientific method he was using.” Devising a hypothesis based on the prevailing understanding of Newtonian physics eventually led Einstein to devise the theory of general relativity .
• Howard Florey “Perhaps the most useful lesson which has come out of the work on penicillin has been the demonstration that success in this field depends on the development and coordinated use of technical methods.” After discovering a mold that prevented the growth of Staphylococcus bacteria, Dr. Alexander Flemimg designed experiments to identify and reproduce it in the lab, thus leading to the development of penicillin .
• James D. Watson “Every time you understand something, religion becomes less likely. Only with the discovery of the double helix and the ensuing genetic revolution have we had grounds for thinking that the powers held traditionally to be the exclusive property of the gods might one day be ours. . . .” By using wire models to conceive a structure for DNA, Watson and Crick crafted a hypothesis for testing combinations of amino acids, X-ray diffraction images, and the current research in atomic physics, resulting in the discovery of DNA’s double helix structure .

Final thoughts

As the cases exemplify, the scientific method is never truly completed, but rather started and restarted. It gave these researchers a structured process that was easily replicated, modified, and built upon. 

While the scientific method may “end” in one context, it never literally ends. When a hypothesis, design, methods, and experiments are revisited, the scientific method simply picks up where it left off. Each time a researcher builds upon previous knowledge, the scientific method is restored with the pieces of past efforts.

By guiding researchers towards objective results based on transparency and reproducibility, the scientific method acts as a defense against bias, superstition, and preconceived notions. As we embrace the scientific method's enduring principles, we ensure that our quest for knowledge remains firmly rooted in reason, evidence, and the pursuit of truth.

The AJE Team

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1.1: The Scientific Method

• Last updated
• Save as PDF
• Page ID 123904

• Teresa Friedrich Finnern
• Norco College

Learning Objectives

• Identify the shared characteristics of the natural sciences.
• Summarize the steps of the scientific method.
• Compare inductive reasoning with deductive reasoning.
• Describe the goals of basic science and applied science.

The Process of Science

Science includes such diverse fields as astronomy, biology, computer sciences, geology, logic, physics, chemistry, and mathematics (Figure \(\PageIndex{1}\)). However, those fields of science related to the physical world and its phenomena and processes are considered natural sciences . Natural sciences could be categorized as astronomy, biology, chemistry, earth science, and physics. One can divide natural sciences further into life sciences, which study living things and include biology, and physical sciences, which study nonliving matter and include astronomy, geology, physics, and chemistry. Some disciplines such as biophysics and biochemistry build on both life and physical sciences and are interdisciplinary. Natural sciences are sometimes referred to as “hard science” because they rely on the use of quantitative data; social sciences that study society and human behavior are more likely to use qualitative assessments to drive investigations and findings.

Not surprisingly, the natural science of biology has many branches or subdisciplines. Cell biologists study cell structure and function, while biologists who study anatomy investigate the structure of an entire organism. Those biologists studying physiology, however, focus on the internal functioning of an organism. Some areas of biology focus on only particular types of living things. For example, botanists explore plants, while zoologists specialize in animals.

Scientific Reasoning

One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. To do this, they use two methods of logical thinking: inductive reasoning and deductive reasoning.

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative (descriptive) or quantitative (numeric), and the raw data can be supplemented with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and the analysis of a large amount of data.

Deductive reasoning ,   or deduction, is the type of logic used in hypothesis-based science. In deductive reason, the pattern of thinking moves in the opposite direction as compared to inductive reasoning; that is, specific results are predicted from a general premise. Deductive reasoning is a form of logical thinking that uses a general principle or law to forecast specific results. From those general principles, a scientist can extrapolate and predict the specific results that would be valid as long as the general principles are valid. Studies in climate change can illustrate this type of reasoning. For example, scientists may predict that if the climate becomes warmer in a particular region, then the distribution of plants and animals should change. These predictions have been made and tested, and many such changes have been found, such as the modification of arable areas for agriculture, with change based on temperature averages. 

Inductive and deductive reasoning are often used in tandem to advance scientific knowledge (Example \(\PageIndex{1}\)) . Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science , which is usually inductive, aims to observe, explore, and discover, while hypothesis-based science , which is usually deductive, begins with a specific question or problem and a potential answer or solution that one can test. The boundary between these two forms of study is often blurred, and most scientific endeavors combine both approaches.

Example \(\PageIndex{1}\)

Here is an example of how the two types of reasoning might be used in similar situations.

In inductive reasoning, where a conclusion is drawn from a number of observations, one might observe that members of a species are not all the same, individuals compete for resources, and species are generally adapted to their environment. This observation could then lead to the conclusion that individuals most adapted to their environment are more likely to survive and pass their traits to the next generation.

In deductive reasoning, which uses a general premise to predict a specific result, one might start with that conclusion as a general premise, then predict the results. For example, from that premise, one might predict that if the average temperature in an ecosystem increases due to climate change, individuals better adapted to warmer temperatures will outcompete those that are not. A scientist could then design a study to test this prediction.

The Scientific Method

Biologists study the living world by posing questions about it and seeking science-based responses. The scientific method is a method of research with defined steps that include experiments and careful observation. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626; Figure \(\PageIndex{2}\)), who set up inductive methods for scientific inquiry. The scientific method is not exclusively used by biologists but can be applied to almost all fields of study as a logical, rational problem-solving method.

It is important to note that even though there are specific steps to the scientific method, the process of science is often more fluid, with scientists going back and forth between steps until they reach their conclusions.

Observation and Question

Scientists are good observers. In the field of biology, naturalists will often will make an observation that leads to a question. A naturalist is a person who studies nature. Naturalists often describe structures, processes, and behavior, either with their eyes or with the use of a tool such as a microscope. A naturalist may not conduct experiments, but they may ask many good questions that can lead to experimentation. Scientists are also very curious. They will research for known answers to their questions or run experiments to learn the answer to their questions.

Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. One Monday morning, a student arrives at class and quickly discovers that the classroom is too warm. That is an observation that also describes a problem: the classroom is too warm. The student then asks a question: “Why is the classroom so warm?”

Proposing a Hypothesis

A hypothesis is an educated guess or a suggested explanation for an event, which can be tested. Sometimes, more than one hypothesis may be proposed. Once a hypothesis has been selected, the student can make a prediction. A prediction is similar to a hypothesis but it typically has the format “If . . . then . . . .”.

For example, one hypothesis might be, “The classroom is warm because no one turned on the air conditioning.” However, there could be other responses to the question, and therefore one may propose other hypotheses. A second hypothesis might be, “The classroom is warm because there is a power failure, and so the air conditioning doesn’t work.” In this case, you would have to test both hypotheses to see if either one could be supported with data.

A hypothesis may become a verified theory . This can happen if it has been repeatedly tested and confirmed, is general, and has inspired many other hypotheses, facts, and experimentations. Not all hypotheses will become theories.

Testing a Hypothesis

A valid hypothesis must be testable. It should also be falsifiable , meaning that it can be disproven by experimental results. Importantly, science does not claim to “prove” anything because scientific understandings are always subject to modification with further information. This step—openness to disproving ideas—is what distinguishes sciences from non-sciences. The presence of the supernatural, for instance, is neither testable nor falsifiable. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment. The control group contains every feature of the experimental group except that it was not manipulated. Therefore, if the results of the experimental group differ from the control group, the difference must be due to the hypothesized manipulation, rather than some outside factor. Look for the variables and controls in the examples that follow. To test the first hypothesis, the student would find out if the air conditioning is on. If the air conditioning is turned on but does not work, there should be another reason, and this hypothesis should be rejected. To test the second hypothesis, the student could check if the lights in the classroom are functional. If so, there is no power failure, and this hypothesis should be rejected. Each hypothesis should be tested by carrying out appropriate experiments. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted; it simply eliminates one hypothesis that is not valid (Figure \(\PageIndex{3}\)). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

While this “warm classroom” example is based on observational results, other hypotheses and experiments might have clearer controls. For instance, a student might attend class on Monday and realize she had difficulty concentrating on the lecture. One observation to explain this occurrence might be, “When I eat breakfast before class, I am better able to pay attention.” The student could then design an experiment with a control to test this hypothesis.

Visual Connection

The scientific method may seem too rigid and structured. It is important to keep in mind that, although scientists often follow this sequence, there is flexibility. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests. Notice, too, that the scientific method can be applied to solving problems that aren’t necessarily scientific in nature (Example \(\PageIndex{2}\)).

Example \(\PageIndex{2}\)

In the example below, the scientific method is used to solve an everyday problem. Match the scientific method steps (numbered items) with the process of solving the everyday problem (lettered items). Based on the results of the experiment, is the hypothesis correct? If it is incorrect, propose some alternative hypotheses.

Steps of the Scientific Method

• Observation
• Hypothesis (answer)

Process of Solving an Everyday Problem

• There is something wrong with the electrical outlet.
• If something is wrong with the outlet, my coffee maker also won’t work when plugged into it.
• My toaster doesn’t toast my bread.
• I plug my coffee maker into the outlet.
• My coffee maker works.
• Why doesn’t my toaster work?

Two Types of Science: Basic Science and Applied Science

The scientific community has been debating for the last few decades about the value of different types of science. Is it valuable to pursue science for the sake of simply gaining knowledge, or does scientific knowledge only have worth if we can apply it to solving a specific problem or to bettering our lives? This question focuses on the differences between two types of science: basic science and applied science.

Basic science or “pure” science seeks to expand knowledge regardless of the short-term application of that knowledge. It is not focused on developing a product or a service of immediate public or commercial value. The immediate goal of basic science is knowledge for knowledge’s sake, though this does not mean that, in the end, it may not result in a practical application.

In contrast, applied science or “technology,” aims to use science to solve real-world problems, making it possible, for example, to improve a crop yield or find a cure for a particular disease. In applied science, the problem is usually defined for the researcher.

Some individuals may perceive applied science as “useful” and basic science as “useless.” A question these people might pose to a scientist advocating knowledge acquisition would be, “What for?” A careful look at the history of science, however, reveals that basic knowledge has resulted in many remarkable applications of great value. Many scientists think that a basic understanding of science is necessary before an application is developed; therefore, applied science relies on the results generated through basic science. Other scientists think that it is time to move on from basic science and instead to find solutions to actual problems. Both approaches are valid. It is true that there are problems that demand immediate attention; however, few solutions would be found without the help of the wide knowledge foundation generated through basic science.

One example of how basic and applied science can work together to solve practical problems occurred after the discovery of DNA structure led to an understanding of the molecular mechanisms governing DNA replication. Strands of DNA, unique in every human, are found in our cells, where they provide the instructions necessary for life. During DNA replication, DNA makes new copies of itself, shortly before a cell divides. Understanding the mechanisms of DNA replication enabled scientists to develop laboratory techniques that are now used to identify genetic diseases, pinpoint individuals who were at a crime scene, and determine paternity. Without basic science, it is unlikely that applied science would exist.

Another example of the link between basic and applied research is the Human Genome Project, a study in which each human chromosome was analyzed and mapped to determine the precise sequence of DNA subunits and the exact location of each gene. (The gene is the basic unit of heredity; an individual’s complete collection of genes is their genome.) Other less complex organisms have also been studied as part of this project in order to gain a better understanding of human chromosomes. The Human Genome Project (Figure \(\PageIndex{4}\)) relied on basic research carried out with simple organisms and, later, with the human genome. An important end goal eventually became using the data for applied research, seeking cures and early diagnoses for genetically related diseases.

While research efforts in both basic science and applied science are usually carefully planned, it is important to note that some discoveries are made by serendipity , that is, by means of a fortunate accident or a lucky surprise. Penicillin was discovered when biologist Alexander Fleming accidentally left a petri dish of Staphylococcus bacteria open. An unwanted mold grew on the dish, killing the bacteria. The mold turned out to be Penicillium , and a new antibiotic was discovered. Even in the highly organized world of science, luck—when combined with an observant, curious mind—can lead to unexpected breakthroughs.

Reporting Scientific Work

Whether scientific research is basic science or applied science, scientists must share their findings in order for other researchers to expand and build upon their discoveries. Collaboration with other scientists—when planning, conducting, and analyzing results—are all important for scientific research. For this reason, important aspects of a scientist’s work are communicating with peers and disseminating results to peers. Scientists can share results by presenting them at a scientific meeting or conference (Figure \(\PageIndex{5}\)), but this approach can reach only the select few who are present. Instead, most scientists present their results in peer-reviewed manuscripts that are published in scientific journals. Peer-reviewed manuscripts are scientific papers that are reviewed by a scientist’s colleagues, or peers. These colleagues are qualified individuals, often experts in the same research area, who judge whether or not the scientist’s work is suitable for publication. The process of peer review helps to ensure that the research described in a scientific paper or grant proposal is original, significant, logical, and thorough. Grant proposals, which are requests for research funding, are also subject to peer review. Scientists publish their work so other scientists can reproduce their experiments under similar or different conditions to expand on the findings. The experimental results must be consistent with the findings of other scientists.

A scientific paper is very different from creative writing. Although creativity is required to design experiments, there are fixed guidelines when it comes to presenting scientific results. First, scientific writing must be brief, concise, and accurate. A scientific paper needs to be succinct but detailed enough to allow peers to reproduce the experiments.

The scientific paper consists of several specific sections—introduction, materials and methods, results, and discussion. This structure is sometimes called the “IMRaD” format, an acronym for Introduction, Method, Results, and Discussion. There are usually acknowledgment and reference sections as well as an abstract (a concise summary) at the beginning of the paper. There might be additional sections depending on the type of paper and the journal where it will be published; for example, some review papers require an outline.

The introduction starts with brief, but broad, background information about what is known in the field. A good introduction also gives the rationale of the work; it justifies the work carried out and also briefly mentions the end of the paper, where the hypothesis or research question driving the research will be presented. The introduction refers to the published scientific work of others and therefore requires citations following the style of the journal. Using the work or ideas of others without proper citation is considered plagiarism .

The materials and methods section includes a complete and accurate description of the substances used, and the method and techniques used by the researchers to gather data. The description should be thorough enough to allow another researcher to repeat the experiment and obtain similar results, but it does not have to be verbose. This section will also include information on how measurements were made and what types of calculations and statistical analyses were used to examine raw data. Although the materials and methods section gives an accurate description of the experiments, it does not discuss them.

Some journals require a results section followed by a discussion section, but it is more common to combine both. If the journal does not allow the combination of both sections, the results section simply narrates the findings without any further interpretation. The results are presented by means of tables or graphs, but no duplicate information should be presented. In the discussion section, the researcher will interpret the results, describe how variables may be related, and attempt to explain the observations. It is indispensable to conduct an extensive literature search to put the results in the context of previously published scientific research. Therefore, proper citations are included in this section as well.

Finally, the conclusion section summarizes the importance of the experimental findings. While the scientific paper almost certainly answered one or more scientific questions that were stated, any good research should lead to more questions. Therefore, a well-done scientific paper leaves doors open for the researcher and others to continue and expand on the findings.

Review articles do not follow the IMRaD format because they do not present original scientific findings (they are not primary literature); instead, they summarize and comment on findings that were published as primary literature and typically include extensive reference sections.

Attributions

Curated and authored by Kammy Algiers using  1.2 (The Process of Science)  from Biology 2e  by OpenStax (licensed CC-BY ).

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Writing a scientific paper.

• Writing a lab report

What is a "good" introduction?

Citing sources in the introduction, "introduction checklist" from: how to write a good scientific paper. chris a. mack. spie. 2018..

• LITERATURE CITED
• Bibliography of guides to scientific writing and presenting
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This is where you describe briefly and clearly why you are writing the paper. The introduction supplies sufficient background information for the reader to understand and evaluate the experiment you did. It also supplies a rationale for the study.

• Present the problem and the proposed solution
• Presents nature and scope of the problem investigated
• Reviews the pertinent literature to orient the reader
• States the method of the experiment
• State the principle results of the experiment

It is important to cite sources in the introduction section of your paper as evidence of the claims you are making. There are ways of citing sources in the text so that the reader can find the full reference in the literature cited section at the end of the paper, yet the flow of the reading is not badly interrupted. Below are some example of how this can be done:     "Smith (1983) found that N-fixing plants could be infected by several different species of Rhizobium."     "Walnut trees are known to be allelopathic (Smith 1949,  Bond et al. 1955, Jones and Green 1963)."     "Although the presence of Rhizobium normally increases the growth of legumes (Nguyen 1987), the opposite effect has been observed (Washington 1999)." Note that articles by one or two authors are always cited in the text using their last names. However, if there are more than two authors, the last name of the 1st author is given followed by the abbreviation et al. which is Latin for "and others". 

From:  https://writingcenter.gmu.edu/guides/imrad-reports-introductions

• Indicate the field of the work, why this field is important, and what has already been done (with proper citations).
• Indicate a gap, raise a research question, or challenge prior work in this territory.
• Outline the purpose and announce the present research, clearly indicating what is novel and why it is significant.
• Avoid: repeating the abstract; providing unnecessary background information; exaggerating the importance of the work; claiming novelty without a proper literature search. 
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Earth is flat.

1. Let's assume that H is true. 2. Let's assume that {A} is true. And if the hypothesis and our set of other assumptions is each true, we may infer that the following conjunction must be true too: 3. H and {A} . STEP 5: INFER THE LOGICAL CONSEQUENCES OF THE ASSUMPTIONS Whether the scientist does so consciously or not, it is at this point when she asks herself the following question: If my hypothesis and other assumptions are true, then what event must I observe in the world? After all, if the assumptions are true, then certain observable events must occur under the specific circumstances determined by the choice of method. The logical consequences of the assumptions are those observable events ( O ). Note that the researcher is not interested in what she may observe, but what she must observe. For instance, if our hypothesis AND our other assumptions are true, does it follow that the patient must have a distended belly? No. The patient may be only one month pregnant or too large for her pregnancy to be seen. Does it follow that the patient will have experienced morning sickness or cravings? No. It is possible to be pregnant and experience neither of those common symptoms. So, if the truth of our hypothesis AND our auxiliary assumptions does not entail either of these observations, what do they entail? What must we observe? While there are several obvious candidates, the observable event ( O ) most relevant for our purposes is this: HCG should be detected in the patient's blood. Here is the logical structure of the test so far: 1. Let's assume that H . 2. Let's assume that {A} . 3. H and {A} . 4. If H and {A} , then O .

This module was supported by National Science Foundation Grants #9981217 and #0127561.

Chapter 2: Psychological Research

The scientific method.

Scientists are engaged in explaining and understanding how the world around them works, and they are able to do so by coming up with theories that generate hypotheses that are testable and falsifiable. Theories that stand up to their tests are retained and refined, while those that do not are discarded or modified. In this way, research enables scientists to separate fact from simple opinion. Having good information generated from research aids in making wise decisions both in public policy and in our personal lives. In this section, you’ll see how psychologists use the scientific method to study and understand behavior.

Scientific research is a critical tool for successfully navigating our complex world. Without it, we would be forced to rely solely on intuition, other people’s authority, and blind luck. While many of us feel confident in our abilities to decipher and interact with the world around us, history is filled with examples of how very wrong we can be when we fail to recognize the need for evidence in supporting claims. At various times in history, we would have been certain that the sun revolved around a flat earth, that the earth’s continents did not move, and that mental illness was caused by possession (Figure 1). It is through systematic scientific research that we divest ourselves of our preconceived notions and superstitions and gain an objective understanding of ourselves and our world.

Figure 1 . Some of our ancestors, believed that trephination—the practice of making a hole in the skull—allowed evil spirits to leave the body, thus curing mental illness.

The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.

While behavior is observable, the mind is not. If someone is crying, we can see behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This module explores how scientific knowledge is generated, and how important that knowledge is in informing decisions in our personal lives and in the public domain.

The Process of Scientific Research

Figure 2 . The scientific method is a process for gathering data and processing information. It provides well-defined steps to standardize how scientific knowledge is gathered through a logical, rational problem-solving method.

Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on.

The basic steps in the scientific method are:

• Observe a natural phenomenon and define a question about it
• Make a hypothesis, or potential solution to the question
• Test the hypothesis
• If the hypothesis is true, find more evidence or find counter-evidence
• If the hypothesis is false, create a new hypothesis or try again
• Draw conclusions and repeat–the scientific method is never-ending, and no result is ever considered perfect

In order to ask an important question that may improve our understanding of the world, a researcher must first observe natural phenomena. By making observations, a researcher can define a useful question. After finding a question to answer, the researcher can then make a prediction (a hypothesis) about what he or she thinks the answer will be. This prediction is usually a statement about the relationship between two or more variables. After making a hypothesis, the researcher will then design an experiment to test his or her hypothesis and evaluate the data gathered. These data will either support or refute the hypothesis. Based on the conclusions drawn from the data, the researcher will then find more evidence to support the hypothesis, look for counter-evidence to further strengthen the hypothesis, revise the hypothesis and create a new experiment, or continue to incorporate the information gathered to answer the research question.

Video 1.  The Scientific Method explains the basic steps taken for most scientific inquiry.

The Basic Principles of the Scientific Method

Two key concepts in the scientific approach are theory and hypothesis. A theory is a well-developed set of ideas that propose an explanation for observed phenomena that can be used to make predictions about future observations. A hypothesis is a testable prediction that is arrived at logically from a theory. It is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests (Figure 3).

Figure 3 . The scientific method of research includes proposing hypotheses, conducting research, and creating or modifying theories based on results.

Other key components in following the scientific method include verifiability, predictability, falsifiability, and fairness. Verifiability means that an experiment must be replicable by another researcher. To achieve verifiability, researchers must make sure to document their methods and clearly explain how their experiment is structured and why it produces certain results.

Predictability in a scientific theory implies that the theory should enable us to make predictions about future events. The precision of these predictions is a measure of the strength of the theory.

Falsifiability refers to whether a hypothesis can be disproved. For a hypothesis to be falsifiable, it must be logically possible to make an observation or do a physical experiment that would show that there is no support for the hypothesis. Even when a hypothesis cannot be shown to be false, that does not necessarily mean it is not valid. Future testing may disprove the hypothesis. This does not mean that a hypothesis has to be shown to be false, just that it can be tested.

To determine whether a hypothesis is supported or not supported, psychological researchers must conduct hypothesis testing using statistics. Hypothesis testing is a type of statistics that determines the probability of a hypothesis being true or false. If hypothesis testing reveals that results were “statistically significant,” this means that there was support for the hypothesis and that the researchers can be reasonably confident that their result was not due to random chance. If the results are not statistically significant, this means that the researchers’ hypothesis was not supported.

Fairness implies that all data must be considered when evaluating a hypothesis. A researcher cannot pick and choose what data to keep and what to discard or focus specifically on data that support or do not support a particular hypothesis. All data must be accounted for, even if they invalidate the hypothesis.

Applying the Scientific Method

To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later module, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race, and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

Remember that a good scientific hypothesis is falsifiable, or capable of being shown to be incorrect. Recall from the introductory module that Sigmund Freud had lots of interesting ideas to explain various human behaviors (Figure 4). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

Figure 4 . Many of the specifics of (a) Freud’s theories, such as (b) his division of the mind into id, ego, and superego, have fallen out of favor in recent decades because they are not falsifiable. In broader strokes, his views set the stage for much of psychological thinking today, such as the unconscious nature of the majority of psychological processes.

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Link to Learning

Want to participate in a study? Visit this Psychological Research on the Net website and click on a link that sounds interesting to you in order to participate in online research.

Why the Scientific Method Is Important for Psychology

The use of the scientific method is one of the main features that separates modern psychology from earlier philosophical inquiries about the mind. Compared to chemistry, physics, and other “natural sciences,” psychology has long been considered one of the “social sciences” because of the subjective nature of the things it seeks to study. Many of the concepts that psychologists are interested in—such as aspects of the human mind, behavior, and emotions—are subjective and cannot be directly measured. Psychologists often rely instead on behavioral observations and self-reported data, which are considered by some to be illegitimate or lacking in methodological rigor. Applying the scientific method to psychology, therefore, helps to standardize the approach to understanding its very different types of information.

The scientific method allows psychological data to be replicated and confirmed in many instances, under different circumstances, and by a variety of researchers. Through replication of experiments, new generations of psychologists can reduce errors and broaden the applicability of theories. It also allows theories to be tested and validated instead of simply being conjectures that could never be verified or falsified. All of this allows psychologists to gain a stronger understanding of how the human mind works.

Scientific articles published in journals and psychology papers written in the style of the American Psychological Association (i.e., in “APA style”) are structured around the scientific method. These papers include an Introduction, which introduces the background information and outlines the hypotheses; a Methods section, which outlines the specifics of how the experiment was conducted to test the hypothesis; a Results section, which includes the statistics that tested the hypothesis and state whether it was supported or not supported, and a Discussion and Conclusion, which state the implications of finding support for, or no support for, the hypothesis. Writing articles and papers that adhere to the scientific method makes it easy for future researchers to repeat the study and attempt to replicate the results.

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, as you will read in the Tuskegee Syphilis Study, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound. This section presents how ethical considerations affect the design and implementation of research conducted today.

Research Involving Human Participants

Any experiment involving the participation of human subjects is governed by extensive, strict guidelines designed to ensure that the experiment does not result in harm. Any research institution that receives federal support for research involving human participants must have access to an institutional review board (IRB) . The IRB is a committee of individuals often made up of members of the institution’s administration, scientists, and community members (Figure 1). The purpose of the IRB is to review proposals for research that involves human participants. The IRB reviews these proposals with the principles mentioned above in mind, and generally, approval from the IRB is required in order for the experiment to proceed.

Figure 5 . An institution’s IRB meets regularly to review experimental proposals that involve human participants. (credit: modification of work by Lowndes Area Knowledge Exchange (LAKE)/Flickr)

An institution’s IRB requires several components in any experiment it approves. For one, each participant must sign an informed consent form before they can participate in the experiment. An informed consent form provides a written description of what participants can expect during the experiment, including potential risks and implications of the research. It also lets participants know that their involvement is completely voluntary and can be discontinued without penalty at any time. Furthermore, informed consent guarantees that any data collected in the experiment will remain completely confidential. In cases where research participants are under the age of 18, the parents or legal guardians are required to sign the informed consent form.

While the informed consent form should be as honest as possible in describing exactly what participants will be doing, sometimes deception is necessary to prevent participants’ knowledge of the exact research question from affecting the results of the study. Deception involves purposely misleading experiment participants in order to maintain the integrity of the experiment, but not to the point where the deception could be considered harmful. For example, if we are interested in how our opinion of someone is affected by their attire, we might use deception in describing the experiment to prevent that knowledge from affecting participants’ responses. In cases where deception is involved, participants must receive a full debriefing upon conclusion of the study—complete, honest information about the purpose of the experiment, how the data collected will be used, the reasons why deception was necessary, and information about how to obtain additional information about the study.

Dig Deeper: Ethics and the Tuskegee Syphilis Study

Unfortunately, the ethical guidelines that exist for research today were not always applied in the past. In 1932, poor, rural, black, male sharecroppers from Tuskegee, Alabama, were recruited to participate in an experiment conducted by the U.S. Public Health Service, with the aim of studying syphilis in black men (Figure 6). In exchange for free medical care, meals, and burial insurance, 600 men agreed to participate in the study. A little more than half of the men tested positive for syphilis, and they served as the experimental group (given that the researchers could not randomly assign participants to groups, this represents a quasi-experiment). The remaining syphilis-free individuals served as the control group. However, those individuals that tested positive for syphilis were never informed that they had the disease.

While there was no treatment for syphilis when the study began, by 1947 penicillin was recognized as an effective treatment for the disease. Despite this, no penicillin was administered to the participants in this study, and the participants were not allowed to seek treatment at any other facilities if they continued in the study. Over the course of 40 years, many of the participants unknowingly spread syphilis to their wives (and subsequently their children born from their wives) and eventually died because they never received treatment for the disease. This study was discontinued in 1972 when the experiment was discovered by the national press (Tuskegee University, n.d.). The resulting outrage over the experiment led directly to the National Research Act of 1974 and the strict ethical guidelines for research on humans described in this chapter. Why is this study unethical? How were the men who participated and their families harmed as a function of this research?

Figure 6 . A participant in the Tuskegee Syphilis Study receives an injection.

Visit this CDC website to learn more about the Tuskegee Syphilis Study.

Research Involving Animal Subjects

Figure 7 . Rats, like the one shown here, often serve as the subjects of animal research.

This does not mean that animal researchers are immune to ethical concerns. Indeed, the humane and ethical treatment of animal research subjects is a critical aspect of this type of research. Researchers must design their experiments to minimize any pain or distress experienced by animals serving as research subjects.

Whereas IRBs review research proposals that involve human participants, animal experimental proposals are reviewed by an Institutional Animal Care and Use Committee (IACUC) . An IACUC consists of institutional administrators, scientists, veterinarians, and community members. This committee is charged with ensuring that all experimental proposals require the humane treatment of animal research subjects. It also conducts semi-annual inspections of all animal facilities to ensure that the research protocols are being followed. No animal research project can proceed without the committee’s approval.

• Modification and adaptation. Provided by : Lumen Learning. License : CC BY-SA: Attribution-ShareAlike
• Psychology and the Scientific Method: From Theory to Conclusion, content on the scientific method principles. Provided by : Boundless. Located at : https://courses.lumenlearning.com/boundless-psychology/ . License : CC BY-SA: Attribution-ShareAlike
• Introduction to Psychological Research, Why is Research Important?, Ethics. Authored by : OpenStax College. Located at : http://cnx.org/contents/[email protected]:Hp5zMFYB@9/Why-Is-Research-Important . License : CC BY: Attribution . License Terms : Download for free at http://cnx.org/contents/[email protected]
• Research picture. Authored by : Mediterranean Center of Medical Sciences. Provided by : Flickr. Located at : https://www.flickr.com/photos/mcmscience/17664002728 . License : CC BY: Attribution

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Scientific Discovery

Scientific discovery is the process or product of successful scientific inquiry. Objects of discovery can be things, events, processes, causes, and properties as well as theories and hypotheses and their features (their explanatory power, for example). Most philosophical discussions of scientific discoveries focus on the generation of new hypotheses that fit or explain given data sets or allow for the derivation of testable consequences. Philosophical discussions of scientific discovery have been intricate and complex because the term “discovery” has been used in many different ways, both to refer to the outcome and to the procedure of inquiry. In the narrowest sense, the term “discovery” refers to the purported “eureka moment” of having a new insight. In the broadest sense, “discovery” is a synonym for “successful scientific endeavor” tout court. Some philosophical disputes about the nature of scientific discovery reflect these terminological variations.

Philosophical issues related to scientific discovery arise about the nature of human creativity, specifically about whether the “eureka moment” can be analyzed and about whether there are rules (algorithms, guidelines, or heuristics) according to which such a novel insight can be brought about. Philosophical issues also arise about the analysis and evaluation of heuristics, about the characteristics of hypotheses worthy of articulation and testing, and, on the meta-level, about the nature and scope of philosophical analysis itself. This essay describes the emergence and development of the philosophical problem of scientific discovery and surveys different philosophical approaches to understanding scientific discovery. In doing so, it also illuminates the meta-philosophical problems surrounding the debates, and, incidentally, the changing nature of philosophy of science.

1. Introduction

2. scientific inquiry as discovery, 3. elements of discovery, 4. pragmatic logics of discovery, 5. the distinction between the context of discovery and the context of justification, 6.1 discovery as abduction, 6.2 heuristic programming, 7. anomalies and the structure of discovery, 8.1 discoverability, 8.2 preliminary appraisal, 8.3 heuristic strategies, 9.1 kinds and features of creativity, 9.2 analogy, 9.3 mental models, 10. machine discovery, 11. social epistemology and discovery, 12. integrated approaches to knowledge generation, other internet resources, related entries.

Philosophical reflection on scientific discovery occurred in different phases. Prior to the 1930s, philosophers were mostly concerned with discoveries in the broad sense of the term, that is, with the analysis of successful scientific inquiry as a whole. Philosophical discussions focused on the question of whether there were any discernible patterns in the production of new knowledge. Because the concept of discovery did not have a specified meaning and was used in a very wide sense, almost all discussions of scientific method and practice could potentially be considered as early contributions to reflections on scientific discovery. In the course of the 18 th century, as philosophy of science and science gradually became two distinct endeavors with different audiences, the term “discovery” became a technical term in philosophical discussions. Different elements of scientific inquiry were specified. Most importantly, during the 19 th century, the generation of new knowledge came to be clearly and explicitly distinguished from its assessment, and thus the conditions for the narrower notion of discovery as the act or process of conceiving new ideas emerged. This distinction was encapsulated in the so-called “context distinction,” between the “context of discovery” and the “context of justification”.

Much of the discussion about scientific discovery in the 20 th century revolved around this distinction It was argued that conceiving a new idea is a non-rational process, a leap of insight that cannot be captured in specific instructions. Justification, by contrast, is a systematic process of applying evaluative criteria to knowledge claims. Advocates of the context distinction argued that philosophy of science is exclusively concerned with the context of justification. The assumption underlying this argument is that philosophy is a normative project; it determines norms for scientific practice. Given this assumption, only the justification of ideas, not their generation, can be the subject of philosophical (normative) analysis. Discovery, by contrast, can only be a topic for empirical study. By definition, the study of discovery is outside the scope of philosophy of science proper.

The introduction of the context distinction and the disciplinary distinction between empirical science studies and normative philosophy of science that was tied to it spawned meta-philosophical disputes. For a long time, philosophical debates about discovery were shaped by the notion that philosophical and empirical analyses are mutually exclusive. Some philosophers insisted, like their predecessors prior to the 1930s, that the philosopher’s tasks include the analysis of actual scientific practices and that scientific resources be used to address philosophical problems. They maintained that it is a legitimate task for philosophy of science to develop a theory of heuristics or problem solving. But this position was the minority view in philosophy of science until the last decades of the 20 th century. Philosophers of discovery were thus compelled to demonstrate that scientific discovery was in fact a legitimate part of philosophy of science. Philosophical reflections about the nature of scientific discovery had to be bolstered by meta-philosophical arguments about the nature and scope of philosophy of science.

Today, however, there is wide agreement that philosophy and empirical research are not mutually exclusive. Not only do empirical studies of actual scientific discoveries in past and present inform philosophical thought about the structure and cognitive mechanisms of discovery, but works in psychology, cognitive science, artificial intelligence and related fields have become integral parts of philosophical analyses of the processes and conditions of the generation of new knowledge. Social epistemology has opened up another perspective on scientific discovery, reconceptualizing knowledge generation as group process.

Prior to the 19 th century, the term “discovery” was used broadly to refer to a new finding, such as a new cure, an unknown territory, an improvement of an instrument, or a new method of measuring longitude. One strand of the discussion about discovery dating back to ancient times concerns the method of analysis as the method of discovery in mathematics and geometry, and, by extension, in philosophy and scientific inquiry. Following the analytic method, we seek to find or discover something – the “thing sought,” which could be a theorem, a solution to a geometrical problem, or a cause – by analyzing it. In the ancient Greek context, analytic methods in mathematics, geometry, and philosophy were not clearly separated; the notion of finding or discovering things by analysis was relevant in all these fields.

In the ensuing centuries, several natural and experimental philosophers, including Avicenna and Zabarella, Bacon and Boyle, the authors of the Port-Royal Logic and Newton, and many others, expounded rules of reasoning and methods for arriving at new knowledge. The ancient notion of analysis still informed these rules and methods. Newton’s famous thirty-first query in the second edition of the Opticks outlines the role of analysis in discovery as follows: “As in Mathematicks, so in Natural Philosophy, the Investigation of difficult Things by the Method of Analysis, ought ever to precede the Method of Composition. This Analysis consists in making Experiments and Observations, and in drawing general Conclusions from them by Induction, and admitting of no Objections against the Conclusions, but such as are taken from Experiments, or other certain Truths … By this way of Analysis we may proceed from Compounds to Ingredients, and from Motions to the Forces producing them; and in general, from Effects to their Causes, and from particular Causes to more general ones, till the Argument end in the most general. This is the Method of Analysis” (Newton 1718, 380, see Koertge 1980, section VI). Early modern accounts of discovery captured knowledge-seeking practices in the study of living and non-living nature, ranging from astronomy and physics to medicine, chemistry, and agriculture. These rich accounts of scientific inquiry were often expounded to bolster particular theories about the nature of matter and natural forces and were not explicitly labeled “methods of discovery ”, yet they are, in fact, accounts of knowledge generation and proper scientific reasoning, covering topics such as the role of the senses in knowledge generation, observation and experimentation, analysis and synthesis, induction and deduction, hypotheses, probability, and certainty.

Bacon’s work is a prominent example. His view of the method of science as it is presented in the Novum Organum showed how best to arrive at knowledge about “form natures” (the most general properties of matter) via a systematic investigation of phenomenal natures. Bacon described how first to collect and organize natural phenomena and experimentally produced facts in tables, how to evaluate these lists, and how to refine the initial results with the help of further trials. Through these steps, the investigator would arrive at conclusions about the “form nature” that produces particular phenomenal natures. Bacon expounded the procedures of constructing and evaluating tables of presences and absences to underpin his matter theory. In addition, in his other writings, such as his natural history Sylva Sylvarum or his comprehensive work on human learning De Augmentis Scientiarium , Bacon exemplified the “art of discovery” with practical examples and discussions of strategies of inquiry.

Like Bacon and Newton, several other early modern authors advanced ideas about how to generate and secure empirical knowledge, what difficulties may arise in scientific inquiry, and how they could be overcome. The close connection between theories about matter and force and scientific methodologies that we find in early modern works was gradually severed. 18 th - and early 19 th -century authors on scientific method and logic cited early modern approaches mostly to model proper scientific practice and reasoning, often creatively modifying them ( section 3 ). Moreover, they developed the earlier methodologies of experimentation, observation, and reasoning into practical guidelines for discovering new phenomena and devising probable hypotheses about cause-effect relations.

It was common in 20 th -century philosophy of science to draw a sharp contrast between those early theories of scientific method and modern approaches. 20 th -century philosophers of science interpreted 17 th - and 18 th -century approaches as generative theories of scientific method. They function simultaneously as guides for acquiring new knowledge and as assessments of the knowledge thus obtained, whereby knowledge that is obtained “in the right way” is considered secure (Laudan 1980; Schaffner 1993: chapter 2). On this view, scientific methods are taken to have probative force (Nickles 1985). According to modern, “consequentialist” theories, propositions must be established by comparing their consequences with observed and experimentally produced phenomena (Laudan 1980; Nickles 1985). It was further argued that, when consequentialist theories were on the rise, the two processes of generation and assessment of an idea or hypothesis became distinct, and the view that the merit of a new idea does not depend on the way in which it was arrived at became widely accepted.

More recent research in history of philosophy of science has shown, however, that there was no such sharp contrast. Consequentialist ideas were advanced throughout the 18 th century, and the early modern generative theories of scientific method and knowledge were more pragmatic than previously assumed. Early modern scholars did not assume that this procedure would lead to absolute certainty. One could only obtain moral certainty for the propositions thus secured.

During the 18 th and 19 th centuries, the different elements of discovery gradually became separated and discussed in more detail. Discussions concerned the nature of observations and experiments, the act of having an insight and the processes of articulating, developing, and testing the novel insight. Philosophical discussion focused on the question of whether and to what extent rules could be devised to guide each of these processes.

Numerous 19 th -century scholars contributed to these discussions, including Claude Bernard, Auguste Comte, George Gore, John Herschel, W. Stanley Jevons, Justus von Liebig, John Stuart Mill, and Charles Sanders Peirce, to name only a few. William Whewell’s work, especially the two volumes of Philosophy of the Inductive Sciences of 1840, is a noteworthy and, later, much discussed contribution to the philosophical debates about scientific discovery because he explicitly distinguished the creative moment or “happy thought” as he called it from other elements of scientific inquiry and because he offered a detailed analysis of the “discoverer’s induction”, i.e., the pursuit and evaluation of the new insight. Whewell’s approach is not unique, but for late 20 th -century philosophers of science, his comprehensive, historically informed philosophy of discovery became a point of orientation in the revival of interest in scientific discovery processes.

For Whewell, discovery comprised three elements: the happy thought, the articulation and development of that thought, and the testing or verification of it. His account was in part a description of the psychological makeup of the discoverer. For instance, he held that only geniuses could have those happy thoughts that are essential to discovery. In part, his account was an account of the methods by which happy thoughts are integrated into the system of knowledge. According to Whewell, the initial step in every discovery is what he called “some happy thought, of which we cannot trace the origin; some fortunate cast of intellect, rising above all rules. No maxims can be given which inevitably lead to discovery” (Whewell 1996 [1840]: 186). An “art of discovery” in the sense of a teachable and learnable skill does not exist according to Whewell. The happy thought builds on the known facts, but according to Whewell it is impossible to prescribe a method for having happy thoughts.

In this sense, happy thoughts are accidental. But in an important sense, scientific discoveries are not accidental. The happy thought is not a wild guess. Only the person whose mind is prepared to see things will actually notice them. The “previous condition of the intellect, and not the single fact, is really the main and peculiar cause of the success. The fact is merely the occasion by which the engine of discovery is brought into play sooner or later. It is, as I have elsewhere said, only the spark which discharges a gun already loaded and pointed; and there is little propriety in speaking of such an accident as the cause why the bullet hits its mark.” (Whewell 1996 [1840]: 189).

Having a happy thought is not yet a discovery, however. The second element of a scientific discovery consists in binding together—“colligating”, as Whewell called it—a set of facts by bringing them under a general conception. Not only does the colligation produce something new, but it also shows the previously known facts in a new light. Colligation involves, on the one hand, the specification of facts through systematic observation, measurements and experiment, and on the other hand, the clarification of ideas through the exposition of the definitions and axioms that are tacitly implied in those ideas. This process is extended and iterative. The scientists go back and forth between binding together the facts, clarifying the idea, rendering the facts more exact, and so forth.

The final part of the discovery is the verification of the colligation involving the happy thought. This means, first and foremost, that the outcome of the colligation must be sufficient to explain the data at hand. Verification also involves judging the predictive power, simplicity, and “consilience” of the outcome of the colligation. “Consilience” refers to a higher range of generality (broader applicability) of the theory (the articulated and clarified happy thought) that the actual colligation produced. Whewell’s account of discovery is not a deductivist system. It is essential that the outcome of the colligation be inferable from the data prior to any testing (Snyder 1997).

Whewell’s theory of discovery clearly separates three elements: the non-analyzable happy thought or eureka moment; the process of colligation which includes the clarification and explication of facts and ideas; and the verification of the outcome of the colligation. His position that the philosophy of discovery cannot prescribe how to think happy thoughts has been a key element of 20 th -century philosophical reflection on discovery. In contrast to many 20 th -century approaches, Whewell’s philosophical conception of discovery also comprises the processes by which the happy thoughts are articulated. Similarly, the process of verification is an integral part of discovery. The procedures of articulation and test are both analyzable according to Whewell, and his conception of colligation and verification serve as guidelines for how the discoverer should proceed. To verify a hypothesis, the investigator needs to show that it accounts for the known facts, that it foretells new, previously unobserved phenomena, and that it can explain and predict phenomena which are explained and predicted by a hypothesis that was obtained through an independent happy thought-cum-colligation (Ducasse 1951).

Whewell’s conceptualization of scientific discovery offers a useful framework for mapping the philosophical debates about discovery and for identifying major issues of concern in 20 th -century philosophical debates. Until the late 20 th century, most philosophers operated with a notion of discovery that is narrower than Whewell’s. In more recent treatments of discovery, however, the scope of the term “discovery” is limited to either the first of these elements, the “happy thought”, or to the happy thought and its initial articulation. In the narrower conception, what Whewell called “verification” is not part of discovery proper. Secondly, until the late 20 th century, there was wide agreement that the eureka moment, narrowly construed, is an unanalyzable, even mysterious leap of insight. The main disagreements concerned the question of whether the process of developing a hypothesis (the “colligation” in Whewell’s terms) is, or is not, a part of discovery proper – and if it is, whether and how this process is guided by rules. Much of the controversies in the 20 th century about the possibility of a philosophy of discovery can be understood against the background of the disagreement about whether the process of discovery does or does not include the articulation and development of a novel thought. Philosophers also disagreed on the issue of whether it is a philosophical task to explicate these rules.

In early 20 th -century logical empiricism, the view that discovery is or at least crucially involves a non-analyzable creative act of a gifted genius was widespread. Alternative conceptions of discovery especially in the pragmatist tradition emphasize that discovery is an extended process, i.e., that the discovery process includes the reasoning processes through which a new insight is articulated and further developed.

In the pragmatist tradition, the term “logic” is used in the broad sense to refer to strategies of human reasoning and inquiry. While the reasoning involved does not proceed according to the principles of demonstrative logic, it is systematic enough to deserve the label “logical”. Proponents of this view argued that traditional (here: syllogistic) logic is an inadequate model of scientific discovery because it misrepresents the process of knowledge generation as grossly as the notion of an “aha moment”.

Early 20 th -century pragmatic logics of discovery can best be described as comprehensive theories of the mental and physical-practical operations involved in knowledge generation, as theories of “how we think” (Dewey 1910). Among the mental operations are classification, determination of what is relevant to an inquiry, and the conditions of communication of meaning; among the physical operations are observation and (laboratory) experiments. These features of scientific discovery are either not or only insufficiently represented by traditional syllogistic logic (Schiller 1917: 236–7).

Philosophers advocating this approach agree that the logic of discovery should be characterized as a set of heuristic principles rather than as a process of applying inductive or deductive logic to a set of propositions. These heuristic principles are not understood to show the path to secure knowledge. Heuristic principles are suggestive rather than demonstrative (Carmichael 1922, 1930). One recurrent feature in these accounts of the reasoning strategies leading to new ideas is analogical reasoning (Schiller 1917; Benjamin 1934, see also section 9.2 .). However, in academic philosophy of science, endeavors to develop more systematically the heuristics guiding discovery processes were soon eclipsed by the advance of the distinction between contexts of discovery and justification.

The distinction between “context of discovery” and “context of justification” dominated and shaped the discussions about discovery in 20 th -century philosophy of science. The context distinction marks the distinction between the generation of a new idea or hypothesis and the defense (test, verification) of it. As the previous sections have shown, the distinction among different elements of scientific inquiry has a long history but in the first half of the 20 th century, the distinction between the different features of scientific inquiry turned into a powerful demarcation criterion between “genuine” philosophy and other fields of science studies, which became potent in philosophy of science. The boundary between context of discovery (the de facto thinking processes) and context of justification (the de jure defense of the correctness of these thoughts) was now understood to determine the scope of philosophy of science, whereby philosophy of science is conceived as a normative endeavor. Advocates of the context distinction argue that the generation of a new idea is an intuitive, nonrational process; it cannot be subject to normative analysis. Therefore, the study of scientists’ actual thinking can only be the subject of psychology, sociology, and other empirical sciences. Philosophy of science, by contrast, is exclusively concerned with the context of justification.

The terms “context of discovery” and “context of justification” are often associated with Hans Reichenbach’s work. Reichenbach’s original conception of the context distinction is quite complex, however (Howard 2006; Richardson 2006). It does not map easily on to the disciplinary distinction mentioned above, because for Reichenbach, philosophy of science proper is partly descriptive. Reichenbach maintains that philosophy of science includes a description of knowledge as it really is. Descriptive philosophy of science reconstructs scientists’ thinking processes in such a way that logical analysis can be performed on them, and it thus prepares the ground for the evaluation of these thoughts (Reichenbach 1938: § 1). Discovery, by contrast, is the object of empirical—psychological, sociological—study. According to Reichenbach, the empirical study of discoveries shows that processes of discovery often correspond to the principle of induction, but this is simply a psychological fact (Reichenbach 1938: 403).

While the terms “context of discovery” and “context of justification” are widely used, there has been ample discussion about how the distinction should be drawn and what their philosophical significance is (c.f. Kordig 1978; Gutting 1980; Zahar 1983; Leplin 1987; Hoyningen-Huene 1987; Weber 2005: chapter 3; Schickore and Steinle 2006). Most commonly, the distinction is interpreted as a distinction between the process of conceiving a theory and the assessment of that theory, specifically the assessment of the theory’s epistemic support. This version of the distinction is not necessarily interpreted as a temporal distinction. In other words, it is not usually assumed that a theory is first fully developed and then assessed. Rather, generation and assessment are two different epistemic approaches to theory: the endeavor to articulate, flesh out, and develop its potential and the endeavor to assess its epistemic worth. Within the framework of the context distinction, there are two main ways of conceptualizing the process of conceiving a theory. The first option is to characterize the generation of new knowledge as an irrational act, a mysterious creative intuition, a “eureka moment”. The second option is to conceptualize the generation of new knowledge as an extended process that includes a creative act as well as some process of articulating and developing the creative idea.

Both of these accounts of knowledge generation served as starting points for arguments against the possibility of a philosophy of discovery. In line with the first option, philosophers have argued that neither is it possible to prescribe a logical method that produces new ideas nor is it possible to reconstruct logically the process of discovery. Only the process of testing is amenable to logical investigation. This objection to philosophies of discovery has been called the “discovery machine objection” (Curd 1980: 207). It is usually associated with Karl Popper’s Logic of Scientific Discovery .

The initial state, the act of conceiving or inventing a theory, seems to me neither to call for logical analysis not to be susceptible of it. The question how it happens that a new idea occurs to a man—whether it is a musical theme, a dramatic conflict, or a scientific theory—may be of great interest to empirical psychology; but it is irrelevant to the logical analysis of scientific knowledge. This latter is concerned not with questions of fact (Kant’s quid facti ?) , but only with questions of justification or validity (Kant’s quid juris ?) . Its questions are of the following kind. Can a statement be justified? And if so, how? Is it testable? Is it logically dependent on certain other statements? Or does it perhaps contradict them? […]Accordingly I shall distinguish sharply between the process of conceiving a new idea, and the methods and results of examining it logically. As to the task of the logic of knowledge—in contradistinction to the psychology of knowledge—I shall proceed on the assumption that it consists solely in investigating the methods employed in those systematic tests to which every new idea must be subjected if it is to be seriously entertained. (Popper 2002 [1934/1959]: 7–8)

With respect to the second way of conceptualizing knowledge generation, many philosophers argue in a similar fashion that because the process of discovery involves an irrational, intuitive process, which cannot be examined logically, a logic of discovery cannot be construed. Other philosophers turn against the philosophy of discovery even though they explicitly acknowledge that discovery is an extended, reasoned process. They present a meta-philosophical objection argument, arguing that a theory of articulating and developing ideas is not a philosophical but a psychological or sociological theory. In this perspective, “discovery” is understood as a retrospective label, which is attributed as a sign of accomplishment to some scientific endeavors. Sociological theories acknowledge that discovery is a collective achievement and the outcome of a process of negotiation through which “discovery stories” are constructed and certain knowledge claims are granted discovery status (Brannigan 1981; Schaffer 1986, 1994).

The impact of the context distinction on 20 th -century studies of scientific discovery and on philosophy of science more generally can hardly be overestimated. The view that the process of discovery (however construed) is outside the scope of philosophy of science proper was widely shared amongst philosophers of science for most of the 20 th century. The last section shows that there were some attempts to develop logics of discovery in the 1920s and 1930s, especially in the pragmatist tradition. But for several decades, the context distinction dictated what philosophy of science should be about and how it should proceed. The dominant view was that theories of mental operations or heuristics had no place in philosophy of science and that, therefore, discovery was not a legitimate topic for philosophy of science. Until the last decades of the 20 th century, there were few attempts to challenge the disciplinary distinction tied to the context distinction. Only during the 1970s did the interest in philosophical approaches to discovery begin to increase again. But the context distinction remained a challenge for philosophies of discovery.

There are several lines of response to the disciplinary distinction tied to the context distinction. Each of these lines of response opens a philosophical perspective on discovery. Each proceeds on the assumption that philosophy of science may legitimately include some form of analysis of actual reasoning patterns as well as information from empirical sciences such as cognitive science, psychology, and sociology. All of these responses reject the idea that discovery is nothing but a mystical event. Discovery is conceived as an analyzable reasoning process, not just as a creative leap by which novel ideas spring into being fully formed. All of these responses agree that the procedures and methods for arriving at new hypotheses and ideas are no guarantee that the hypothesis or idea that is thus formed is necessarily the best or the correct one. Nonetheless, it is the task of philosophy of science to provide rules for making this process better. All of these responses can be described as theories of problem solving, whose ultimate goal is to make the generation of new ideas and theories more efficient.

But the different approaches to scientific discovery employ different terminologies. In particular, the term “logic” of discovery is sometimes used in a narrow sense and sometimes broadly understood. In the narrow sense, “logic” of discovery is understood to refer to a set of formal, generally applicable rules by which novel ideas can be mechanically derived from existing data. In the broad sense, “logic” of discovery refers to the schematic representation of reasoning procedures. “Logical” is just another term for “rational”. Moreover, while each of these responses combines philosophical analyses of scientific discovery with empirical research on actual human cognition, different sets of resources are mobilized, ranging from AI research and cognitive science to historical studies of problem-solving procedures. Also, the responses parse the process of scientific inquiry differently. Often, scientific inquiry is regarded as having two aspects, viz. generation and assessments of new ideas. At times, however, scientific inquiry is regarded as having three aspects, namely generation, pursuit or articulation, and assessment of knowledge. In the latter framework, the label “discovery” is sometimes used to refer just to generation and sometimes to refer to both generation and pursuit.

One response to the challenge of the context distinction draws on a broad understanding of the term “logic” to argue that we cannot but admit a general, domain-neutral logic if we do not want to assume that the success of science is a miracle (Jantzen 2016) and that a logic of scientific discovery can be developed ( section 6 ). Another response, drawing on a narrow understanding of the term “logic”, is to concede that there is no logic of discovery, i.e., no algorithm for generating new knowledge, but that the process of discovery follows an identifiable, analyzable pattern ( section 7 ).

Others argue that discovery is governed by a methodology . The methodology of discovery is a legitimate topic for philosophical analysis ( section 8 ). Yet another response assumes that discovery is or at least involves a creative act. Drawing on resources from cognitive science, neuroscience, computational research, and environmental and social psychology, philosophers have sought to demystify the cognitive processes involved in the generation of new ideas. Philosophers who take this approach argue that scientific creativity is amenable to philosophical analysis ( section 9.1 ).

All these responses assume that there is more to discovery than a eureka moment. Discovery comprises processes of articulating, developing, and assessing the creative thought, as well as the scientific community’s adjudication of what does, and does not, count as “discovery” (Arabatzis 1996). These are the processes that can be examined with the tools of philosophical analysis, augmented by input from other fields of science studies such as sociology, history, or cognitive science.

6. Logics of discovery after the context distinction

One way of responding to the demarcation criterion described above is to argue that discovery is a topic for philosophy of science because it is a logical process after all. Advocates of this approach to the logic of discovery usually accept the overall distinction between the two processes of conceiving and testing a hypothesis. They also agree that it is impossible to put together a manual that provides a formal, mechanical procedure through which innovative concepts or hypotheses can be derived: There is no discovery machine. But they reject the view that the process of conceiving a theory is a creative act, a mysterious guess, a hunch, a more or less instantaneous and random process. Instead, they insist that both conceiving and testing hypotheses are processes of reasoning and systematic inference, that both of these processes can be represented schematically, and that it is possible to distinguish better and worse paths to new knowledge.

This line of argument has much in common with the logics of discovery described in section 4 above but it is now explicitly pitched against the disciplinary distinction tied to the context distinction. There are two main ways of developing this argument. The first is to conceive of discovery in terms of abductive reasoning ( section 6.1 ). The second is to conceive of discovery in terms of problem-solving algorithms, whereby heuristic rules aid the processing of available data and enhance the success in finding solutions to problems ( section 6.2 ). Both lines of argument rely on a broad conception of logic, whereby the “logic” of discovery amounts to a schematic account of the reasoning processes involved in knowledge generation.

One argument, elaborated prominently by Norwood R. Hanson, is that the act of discovery—here, the act of suggesting a new hypothesis—follows a distinctive logical pattern, which is different from both inductive logic and the logic of hypothetico-deductive reasoning. The special logic of discovery is the logic of abductive or “retroductive” inferences (Hanson 1958). The argument that it is through an act of abductive inferences that plausible, promising scientific hypotheses are devised goes back to C.S. Peirce. This version of the logic of discovery characterizes reasoning processes that take place before a new hypothesis is ultimately justified. The abductive mode of reasoning that leads to plausible hypotheses is conceptualized as an inference beginning with data or, more specifically, with surprising or anomalous phenomena.

In this view, discovery is primarily a process of explaining anomalies or surprising, astonishing phenomena. The scientists’ reasoning proceeds abductively from an anomaly to an explanatory hypothesis in light of which the phenomena would no longer be surprising or anomalous. The outcome of this reasoning process is not one single specific hypothesis but the delineation of a type of hypotheses that is worthy of further attention (Hanson 1965: 64). According to Hanson, the abductive argument has the following schematic form (Hanson 1960: 104):

• Some surprising, astonishing phenomena p 1 , p 2 , p 3 … are encountered.
• But p 1 , p 2 , p 3 … would not be surprising were an hypothesis of H ’s type to obtain. They would follow as a matter of course from something like H and would be explained by it.
• Therefore there is good reason for elaborating an hypothesis of type H—for proposing it as a possible hypothesis from whose assumption p 1 , p 2 , p 3 … might be explained.

Drawing on the historical record, Hanson argues that several important discoveries were made relying on abductive reasoning, such as Kepler’s discovery of the elliptic orbit of Mars (Hanson 1958). It is now widely agreed, however, that Hanson’s reconstruction of the episode is not a historically adequate account of Kepler’s discovery (Lugg 1985). More importantly, while there is general agreement that abductive inferences are frequent in both everyday and scientific reasoning, these inferences are no longer considered as logical inferences. Even if one accepts Hanson’s schematic representation of the process of identifying plausible hypotheses, this process is a “logical” process only in the widest sense whereby the term “logical” is understood as synonymous with “rational”. Notably, some philosophers have even questioned the rationality of abductive inferences (Koehler 1991; Brem and Rips 2000).

Another argument against the above schema is that it is too permissive. There will be several hypotheses that are explanations for phenomena p 1 , p 2 , p 3 …, so the fact that a particular hypothesis explains the phenomena is not a decisive criterion for developing that hypothesis (Harman 1965; see also Blackwell 1969). Additional criteria are required to evaluate the hypothesis yielded by abductive inferences.

Finally, it is worth noting that the schema of abductive reasoning does not explain the very act of conceiving a hypothesis or hypothesis-type. The processes by which a new idea is first articulated remain unanalyzed in the above schema. The schema focuses on the reasoning processes by which an exploratory hypothesis is assessed in terms of its merits and promise (Laudan 1980; Schaffner 1993).

In more recent work on abduction and discovery, two notions of abduction are sometimes distinguished: the common notion of abduction as inference to the best explanation (selective abduction) and creative abduction (Magnani 2000, 2009). Selective abduction—the inference to the best explanation—involves selecting a hypothesis from a set of known hypotheses. Medical diagnosis exemplifies this kind of abduction. Creative abduction, by contrast, involves generating a new, plausible hypothesis. This happens, for instance, in medical research, when the notion of a new disease is articulated. However, it is still an open question whether this distinction can be drawn, or whether there is a more gradual transition from selecting an explanatory hypothesis from a familiar domain (selective abduction) to selecting a hypothesis that is slightly modified from the familiar set and to identifying a more drastically modified or altered assumption.

Another recent suggestion is to broaden Peirce’s original account of abduction and to include not only verbal information but also non-verbal mental representations, such as visual, auditory, or motor representations. In Thagard’s approach, representations are characterized as patterns of activity in mental populations (see also section 9.3 below). The advantage of the neural account of human reasoning is that it covers features such as the surprise that accompanies the generation of new insights or the visual and auditory representations that contribute to it. Surprise, for instance, could be characterized as resulting from rapid changes in activation of the node in a neural network representing the “surprising” element (Thagard and Stewart 2011). If all mental representations can be characterized as patterns of firing in neural populations, abduction can be analyzed as the combination or “convolution” (Thagard) of patterns of neural activity from disjoint or overlapping patterns of activity (Thagard 2010).

The concern with the logic of discovery has also motivated research on artificial intelligence at the intersection of philosophy of science and cognitive science. In this approach, scientific discovery is treated as a form of problem-solving activity (Simon 1973; see also Newell and Simon 1971), whereby the systematic aspects of problem solving are studied within an information-processing framework. The aim is to clarify with the help of computational tools the nature of the methods used to discover scientific hypotheses. These hypotheses are regarded as solutions to problems. Philosophers working in this tradition build computer programs employing methods of heuristic selective search (e.g., Langley et al. 1987). In computational heuristics, search programs can be described as searches for solutions in a so-called “problem space” in a certain domain. The problem space comprises all possible configurations in that domain (e.g., for chess problems, all possible arrangements of pieces on a board of chess). Each configuration is a “state” of the problem space. There are two special states, namely the goal state, i.e., the state to be reached, and the initial state, i.e., the configuration at the starting point from which the search begins. There are operators, which determine the moves that generate new states from the current state. There are path constraints, which limit the permitted moves. Problem solving is the process of searching for a solution of the problem of how to generate the goal state from an initial state. In principle, all states can be generated by applying the operators to the initial state, then to the resulting state, until the goal state is reached (Langley et al. 1987: chapter 9). A problem solution is a sequence of operations leading from the initial to the goal state.

The basic idea behind computational heuristics is that rules can be identified that serve as guidelines for finding a solution to a given problem quickly and efficiently by avoiding undesired states of the problem space. These rules are best described as rules of thumb. The aim of constructing a logic of discovery thus becomes the aim of constructing a heuristics for the efficient search for solutions to problems. The term “heuristic search” indicates that in contrast to algorithms, problem-solving procedures lead to results that are merely provisional and plausible. A solution is not guaranteed, but heuristic searches are advantageous because they are more efficient than exhaustive random trial and error searches. Insofar as it is possible to evaluate whether one set of heuristics is better—more efficacious—than another, the logic of discovery turns into a normative theory of discovery.

Arguably, because it is possible to reconstruct important scientific discovery processes with sets of computational heuristics, the scientific discovery process can be considered as a special case of the general mechanism of information processing. In this context, the term “logic” is not used in the narrow sense of a set of formal, generally applicable rules to draw inferences but again in a broad sense as a label for a set of procedural rules.

The computer programs that embody the principles of heuristic searches in scientific inquiry simulate the paths that scientists followed when they searched for new theoretical hypotheses. Computer programs such as BACON (Simon et al. 1981) and KEKADA (Kulkarni and Simon 1988) utilize sets of problem-solving heuristics to detect regularities in given data sets. The program would note, for instance, that the values of a dependent term are constant or that a set of values for a term x and a set of values for a term y are linearly related. It would thus “infer” that the dependent term always has that value or that a linear relation exists between x and y . These programs can “make discoveries” in the sense that they can simulate successful discoveries such as Kepler’s third law (BACON) or the Krebs cycle (KEKADA).

Computational theories of scientific discoveries have helped identify and clarify a number of problem-solving strategies. An example of such a strategy is heuristic means-ends analysis, which involves identifying specific differences between the present and the goal situation and searches for operators (processes that will change the situation) that are associated with the differences that were detected. Another important heuristic is to divide the problem into sub-problems and to begin solving the one with the smallest number of unknowns to be determined (Simon 1977). Computational approaches have also highlighted the extent to which the generation of new knowledge draws on existing knowledge that constrains the development of new hypotheses.

As accounts of scientific discoveries, the early computational heuristics have some limitations. Compared to the problem spaces given in computational heuristics, the complex problem spaces for scientific problems are often ill defined, and the relevant search space and goal state must be delineated before heuristic assumptions could be formulated (Bechtel and Richardson 1993: chapter 1). Because a computer program requires the data from actual experiments, the simulations cover only certain aspects of scientific discoveries; in particular, it cannot determine by itself which data is relevant, which data to relate and what form of law it should look for (Gillies 1996). However, as a consequence of the rise of so-called “deep learning” methods in data-intensive science, there is renewed philosophical interest in the question of whether machines can make discoveries ( section 10 ).

Many philosophers maintain that discovery is a legitimate topic for philosophy of science while abandoning the notion that there is a logic of discovery. One very influential approach is Thomas Kuhn’s analysis of the emergence of novel facts and theories (Kuhn 1970 [1962]: chapter 6). Kuhn identifies a general pattern of discovery as part of his account of scientific change. A discovery is not a simple act, but an extended, complex process, which culminates in paradigm changes. Paradigms are the symbolic generalizations, metaphysical commitments, values, and exemplars that are shared by a community of scientists and that guide the research of that community. Paradigm-based, normal science does not aim at novelty but instead at the development, extension, and articulation of accepted paradigms. A discovery begins with an anomaly, that is, with the recognition that the expectations induced by an established paradigm are being violated. The process of discovery involves several aspects: observations of an anomalous phenomenon, attempts to conceptualize it, and changes in the paradigm so that the anomaly can be accommodated.

It is the mark of success of normal science that it does not make transformative discoveries, and yet such discoveries come about as a consequence of normal, paradigm-guided science. The more detailed and the better developed a paradigm, the more precise are its predictions. The more precisely the researchers know what to expect, the better they are able to recognize anomalous results and violations of expectations:

novelty ordinarily emerges only for the man who, knowing with precision what he should expect, is able to recognize that something has gone wrong. Anomaly appears only against the background provided by the paradigm. (Kuhn 1970 [1962]: 65)

Drawing on several historical examples, Kuhn argues that it is usually impossible to identify the very moment when something was discovered or even the individual who made the discovery. Kuhn illustrates these points with the discovery of oxygen (see Kuhn 1970 [1962]: 53–56). Oxygen had not been discovered before 1774 and had been discovered by 1777. Even before 1774, Lavoisier had noticed that something was wrong with phlogiston theory, but he was unable to move forward. Two other investigators, C. W. Scheele and Joseph Priestley, independently identified a gas obtained from heating solid substances. But Scheele’s work remained unpublished until after 1777, and Priestley did not identify his substance as a new sort of gas. In 1777, Lavoisier presented the oxygen theory of combustion, which gave rise to fundamental reconceptualization of chemistry. But according to this theory as Lavoisier first presented it, oxygen was not a chemical element. It was an atomic “principle of acidity” and oxygen gas was a combination of that principle with caloric. According to Kuhn, all of these developments are part of the discovery of oxygen, but none of them can be singled out as “the” act of discovery.

In pre-paradigmatic periods or in times of paradigm crisis, theory-induced discoveries may happen. In these periods, scientists speculate and develop tentative theories, which may lead to novel expectations and experiments and observations to test whether these expectations can be confirmed. Even though no precise predictions can be made, phenomena that are thus uncovered are often not quite what had been expected. In these situations, the simultaneous exploration of the new phenomena and articulation of the tentative hypotheses together bring about discovery.

In cases like the discovery of oxygen, by contrast, which took place while a paradigm was already in place, the unexpected becomes apparent only slowly, with difficulty, and against some resistance. Only gradually do the anomalies become visible as such. It takes time for the investigators to recognize “both that something is and what it is” (Kuhn 1970 [1962]: 55). Eventually, a new paradigm becomes established and the anomalous phenomena become the expected phenomena.

Recent studies in cognitive neuroscience of brain activity during periods of conceptual change support Kuhn’s view that conceptual change is hard to achieve. These studies examine the neural processes that are involved in the recognition of anomalies and compare them with the brain activity involved in the processing of information that is consistent with preferred theories. The studies suggest that the two types of data are processed differently (Dunbar et al. 2007).

8. Methodologies of discovery

Advocates of the view that there are methodologies of discovery use the term “logic” in the narrow sense of an algorithmic procedure to generate new ideas. But like the AI-based theories of scientific discovery described in section 6 , methodologies of scientific discovery interpret the concept “discovery” as a label for an extended process of generating and articulating new ideas and often describe the process in terms of problem solving. In these approaches, the distinction between the contexts of discovery and the context of justification is challenged because the methodology of discovery is understood to play a justificatory role. Advocates of a methodology of discovery usually rely on a distinction between different justification procedures, justification involved in the process of generating new knowledge and justification involved in testing it. Consequential or “strong” justifications are methods of testing. The justification involved in discovery, by contrast, is conceived as generative (as opposed to consequential) justification ( section 8.1 ) or as weak (as opposed to strong) justification ( section 8.2 ). Again, some terminological ambiguity exists because according to some philosophers, there are three contexts, not two: Only the initial conception of a new idea (the creative act is the context of discovery proper, and between it and justification there exists a separate context of pursuit (Laudan 1980). But many advocates of methodologies of discovery regard the context of pursuit as an integral part of the process of justification. They retain the notion of two contexts and re-draw the boundaries between the contexts of discovery and justification as they were drawn in the early 20 th century.

The methodology of discovery has sometimes been characterized as a form of justification that is complementary to the methodology of testing (Nickles 1984, 1985, 1989). According to the methodology of testing, empirical support for a theory results from successfully testing the predictive consequences derived from that theory (and appropriate auxiliary assumptions). In light of this methodology, justification for a theory is “consequential justification,” the notion that a hypothesis is established if successful novel predictions are derived from the theory or claim. Generative justification complements consequential justification. Advocates of generative justification hold that there exists an important form of justification in science that involves reasoning to a claim from data or previously established results more generally.

One classic example for a generative methodology is the set of Newton’s rules for the study of natural philosophy. According to these rules, general propositions are established by deducing them from the phenomena. The notion of generative justification seeks to preserve the intuition behind classic conceptions of justification by deduction. Generative justification amounts to the rational reconstruction of the discovery path in order to establish its discoverability had the researchers known what is known now, regardless of how it was first thought of (Nickles 1985, 1989). The reconstruction demonstrates in hindsight that the claim could have been discovered in this manner had the necessary information and techniques been available. In other words, generative justification—justification as “discoverability” or “potential discovery”—justifies a knowledge claim by deriving it from results that are already established. While generative justification does not retrace exactly those steps of the actual discovery path that were actually taken, it is a better representation of scientists’ actual practices than consequential justification because scientists tend to construe new claims from available knowledge. Generative justification is a weaker version of the traditional ideal of justification by deduction from the phenomena. Justification by deduction from the phenomena is complete if a theory or claim is completely determined from what we already know. The demonstration of discoverability results from the successful derivation of a claim or theory from the most basic and most solidly established empirical information.

Discoverability as described in the previous paragraphs is a mode of justification. Like the testing of novel predictions derived from a hypothesis, generative justification begins when the phase of finding and articulating a hypothesis worthy of assessing is drawing to a close. Other approaches to the methodology of discovery are directly concerned with the procedures involved in devising new hypotheses. The argument in favor of this kind of methodology is that the procedures of devising new hypotheses already include elements of appraisal. These preliminary assessments have been termed “weak” evaluation procedures (Schaffner 1993). Weak evaluations are relevant during the process of devising a new hypothesis. They provide reasons for accepting a hypothesis as promising and worthy of further attention. Strong evaluations, by contrast, provide reasons for accepting a hypothesis as (approximately) true or confirmed. Both “generative” and “consequential” testing as discussed in the previous section are strong evaluation procedures. Strong evaluation procedures are rigorous and systematically organized according to the principles of hypothesis derivation or H-D testing. A methodology of preliminary appraisal, by contrast, articulates criteria for the evaluation of a hypothesis prior to rigorous derivation or testing. It aids the decision about whether to take that hypothesis seriously enough to develop it further and test it. For advocates of this version of the methodology of discovery, it is the task of philosophy of science to characterize sets of constraints and methodological rules guiding the complex process of prior-to-test evaluation of hypotheses.

In contrast to the computational approaches discussed above, strategies of preliminary appraisal are not regarded as subject-neutral but as specific to particular fields of study. Philosophers of biology, for instance, have developed a fine-grained framework to account for the generation and preliminary evaluation of biological mechanisms (Darden 2002; Craver 2002; Bechtel and Richardson 1993; Craver and Darden 2013). Some philosophers have suggested that the phase of preliminary appraisal be further divided into two phases, the phase of appraising and the phase of revising. According to Lindley Darden, the phases of generation, appraisal and revision of descriptions of mechanisms can be characterized as reasoning processes governed by reasoning strategies. Different reasoning strategies govern the different phases (Darden 1991, 2002; Craver 2002; Darden 2009). The generation of hypotheses about mechanisms, for instance, is governed by the strategy of “schema instantiation” (see Darden 2002). The discovery of the mechanism of protein synthesis involved the instantiation of an abstract schema for chemical reactions: reactant 1 + reactant 2 = product. The actual mechanism of protein synthesis was found through specification and modification of this schema.

Neither of these strategies is deemed necessary for discovery, and they are not prescriptions for biological research. Rather, these strategies are deemed sufficient for the discovery of mechanisms. The methodology of the discovery of mechanisms is an extrapolation from past episodes of research on mechanisms and the result of a synthesis of rational reconstructions of several of these historical episodes. The methodology of discovery is weakly normative in the sense that the strategies for the discovery of mechanisms that were successful in the past may prove useful in future biological research (Darden 2002).

As philosophers of science have again become more attuned to actual scientific practices, interest in heuristic strategies has also been revived. Many analysts now agree that discovery processes can be regarded as problem solving activities, whereby a discovery is a solution to a problem. Heuristics-based methodologies of discovery are neither purely subjective and intuitive nor algorithmic or formalizable; the point is that reasons can be given for pursuing one or the other problem-solving strategy. These rules are open and do not guarantee a solution to a problem when applied (Ippoliti 2018). On this view, scientific researchers are no longer seen as Kuhnian “puzzle solvers” but as problem solvers and decision makers in complex, variable, and changing environments (Wimsatt 2007).

Philosophers of discovery working in this tradition draw on a growing body of literature in cognitive psychology, management science, operations research, and economy on human reasoning and decision making in contexts with limited information, under time constraints, and with sub-optimal means (Gigerenzer & Sturm 2012). Heuristic strategies characterized in these studies, such as Gigerenzer’s “tools to theory heuristic” are then applied to understand scientific knowledge generation (Gigerenzer 1992, Nickles 2018). Other analysts specify heuristic strategies in a range of scientific fields, including climate science, neurobiology, and clinical medicine (Gramelsberger 2011, Schaffner 2008, Gillies 2018). Finally, in analytic epistemology, formal methods are developed to identify and assess distinct heuristic strategies currently in use, such as Bayesian reverse engineering in cognitive science (Zednik and Jäkel 2016).

As the literature on heuristics continues to grow, it has become clear that the term “heuristics” is itself used in a variety of different ways. (For a valuable taxonomy of meanings of “heuristic,” see Chow 2015, see also Ippoliti 2018.) Moreover, as in the context of earlier debates about computational heuristics, debates continue about the limitations of heuristics. The use of heuristics may come at a cost if heuristics introduce systematic biases (Wimsatt 2007). Some philosophers thus call for general principles for the evaluation of heuristic strategies (Hey 2016).

9. Cognitive perspectives on discovery

The approaches to scientific discovery presented in the previous sections focus on the adoption, articulation, and preliminary evaluation of ideas or hypotheses prior to rigorous testing, not on how a novel hypothesis or idea is first thought up. For a long time, the predominant view among philosophers of discovery was that the initial step of discovery is a mysterious intuitive leap of the human mind that cannot be analyzed further. More recent accounts of discovery informed by evolutionary biology also do not explicate how new ideas are formed. The generation of new ideas is akin to random, blind variations of thought processes, which have to be inspected by the critical mind and assessed as neutral, productive, or useless (Campbell 1960; see also Hull 1988), but the key processes by which new ideas are generated are left unanalyzed.

With the recent rapprochement among philosophy of mind, cognitive science and psychology and the increased integration of empirical research into philosophy of science, these processes have been submitted to closer analysis, and philosophical studies of creativity have seen a surge of interest (e.g. Paul & Kaufman 2014a). The distinctive feature of these studies is that they integrate philosophical analyses with empirical work from cognitive science, psychology, evolutionary biology, and computational neuroscience (Thagard 2012). Analysts have distinguished different kinds and different features of creative thinking and have examined certain features in depth, and from new angles. Recent philosophical research on creativity comprises conceptual analyses and integrated approaches based on the assumption that creativity can be analyzed and that empirical research can contribute to the analysis (Paul & Kaufman 2014b). Two key elements of the cognitive processes involved in creative thinking that have been in the focus of philosophical analysis are analogies ( section 9.2 ) and mental models ( section 9.3 ).

General definitions of creativity highlight novelty or originality and significance or value as distinctive features of a creative act or product (Sternberg & Lubart 1999, Kieran 2014, Paul & Kaufman 2014b, although see Hills & Bird 2019). Different kinds of creativity can be distinguished depending on whether the act or product is novel for a particular individual or entirely novel. Psychologist Margaret Boden distinguishes between psychological creativity (P-creativity) and historical creativity (H-creativity). P-creativity is a development that is new, surprising and important to the particular person who comes up with it. H-creativity, by contrast, is radically novel, surprising, and important—it is generated for the first time (Boden 2004). Further distinctions have been proposed, such as anthropological creativity (construed as a human condition) and metaphysical creativity, a radically new thought or action in the sense that it is unaccounted for by antecedents and available knowledge, and thus constitutes a radical break with the past (Kronfeldner 2009, drawing on Hausman 1984).

Psychological studies analyze the personality traits and creative individuals’ behavioral dispositions that are conducive to creative thinking. They suggest that creative scientists share certain distinct personality traits, including confidence, openness, dominance, independence, introversion, as well as arrogance and hostility. (For overviews of recent studies on personality traits of creative scientists, see Feist 1999, 2006: chapter 5).

Recent work on creativity in philosophy of mind and cognitive science offers substantive analyses of the cognitive and neural mechanisms involved in creative thinking (Abrams 2018, Minai et al 2022) and critical scrutiny of the romantic idea of genius creativity as something deeply mysterious (Blackburn 2014). Some of this research aims to characterize features that are common to all creative processes, such as Thagard and Stewart’s account according to which creativity results from combinations of representations (Thagard & Stewart 2011, but see Pasquale and Poirier 2016). Other research aims to identify the features that are distinctive of scientific creativity as opposed to other forms of creativity, such as artistic creativity or creative technological invention (Simonton 2014).

Many philosophers of science highlight the role of analogy in the development of new knowledge, whereby analogy is understood as a process of bringing ideas that are well understood in one domain to bear on a new domain (Thagard 1984; Holyoak and Thagard 1996). An important source for philosophical thought about analogy is Mary Hesse’s conception of models and analogies in theory construction and development. In this approach, analogies are similarities between different domains. Hesse introduces the distinction between positive, negative, and neutral analogies (Hesse 1966: 8). If we consider the relation between gas molecules and a model for gas, namely a collection of billiard balls in random motion, we will find properties that are common to both domains (positive analogy) as well as properties that can only be ascribed to the model but not to the target domain (negative analogy). There is a positive analogy between gas molecules and a collection of billiard balls because both the balls and the molecules move randomly. There is a negative analogy between the domains because billiard balls are colored, hard, and shiny but gas molecules do not have these properties. The most interesting properties are those properties of the model about which we do not know whether they are positive or negative analogies. This set of properties is the neutral analogy. These properties are the significant properties because they might lead to new insights about the less familiar domain. From our knowledge about the familiar billiard balls, we may be able to derive new predictions about the behavior of gas molecules, which we could then test.

Hesse offers a more detailed analysis of the structure of analogical reasoning through the distinction between horizontal and vertical analogies between domains. Horizontal analogies between two domains concern the sameness or similarity between properties of both domains. If we consider sound and light waves, there are similarities between them: sound echoes, light reflects; sound is loud, light is bright, both sound and light are detectable by our senses. There are also relations among the properties within one domain, such as the causal relation between sound and the loud tone we hear and, analogously, between physical light and the bright light we see. These analogies are vertical analogies. For Hesse, vertical analogies hold the key for the construction of new theories.

Analogies play several roles in science. Not only do they contribute to discovery but they also play a role in the development and evaluation of scientific theories. Current discussions about analogy and discovery have expanded and refined Hesse’s approach in various ways. Some philosophers have developed criteria for evaluating analogy arguments (Bartha 2010). Other work has identified highly significant analogies that were particularly fruitful for the advancement of science (Holyoak and Thagard 1996: 186–188; Thagard 1999: chapter 9). The majority of analysts explore the features of the cognitive mechanisms through which aspects of a familiar domain or source are applied to an unknown target domain in order to understand what is unknown. According to the influential multi-constraint theory of analogical reasoning developed by Holyoak and Thagard, the transfer processes involved in analogical reasoning (scientific and otherwise) are guided or constrained in three main ways: 1) by the direct similarity between the elements involved; 2) by the structural parallels between source and target domain; as well as 3) by the purposes of the investigators, i.e., the reasons why the analogy is considered. Discovery, the formulation of a new hypothesis, is one such purpose.

“In vivo” investigations of scientists reasoning in their laboratories have not only shown that analogical reasoning is a key component of scientific practice, but also that the distance between source and target depends on the purpose for which analogies are sought. Scientists trying to fix experimental problems draw analogies between targets and sources from highly similar domains. In contrast, scientists attempting to formulate new models or concepts draw analogies between less similar domains. Analogies between radically different domains, however, are rare (Dunbar 1997, 2001).

In current cognitive science, human cognition is often explored in terms of model-based reasoning. The starting point of this approach is the notion that much of human reasoning, including probabilistic and causal reasoning as well as problem solving takes place through mental modeling rather than through the application of logic or methodological criteria to a set of propositions (Johnson-Laird 1983; Magnani et al. 1999; Magnani and Nersessian 2002). In model-based reasoning, the mind constructs a structural representation of a real-world or imaginary situation and manipulates this structure. In this perspective, conceptual structures are viewed as models and conceptual innovation as constructing new models through various modeling operations. Analogical reasoning—analogical modeling—is regarded as one of three main forms of model-based reasoning that appear to be relevant for conceptual innovation in science. Besides analogical modeling, visual modeling and simulative modeling or thought experiments also play key roles (Nersessian 1992, 1999, 2009). These modeling practices are constructive in that they aid the development of novel mental models. The key elements of model-based reasoning are the call on knowledge of generative principles and constraints for physical models in a source domain and the use of various forms of abstraction. Conceptual innovation results from the creation of new concepts through processes that abstract and integrate source and target domains into new models (Nersessian 2009).

Some critics have argued that despite the large amount of work on the topic, the notion of mental model is not sufficiently clear. Thagard seeks to clarify the concept by characterizing mental models in terms of neural processes (Thagard 2010). In his approach, mental models are produced through complex patterns of neural firing, whereby the neurons and the interconnections between them are dynamic and changing. A pattern of firing neurons is a representation when there is a stable causal correlation between the pattern or activation and the thing that is represented. In this research, questions about the nature of model-based reasoning are transformed into questions about the brain mechanisms that produce mental representations.

The above sections again show that the study of scientific discovery integrates different approaches, combining conceptual analysis of processes of knowledge generation with empirical work on creativity, drawing heavily and explicitly on current research in psychology and cognitive science, and on in vivo laboratory observations, as well as brain imaging techniques (Kounios & Beeman 2009, Thagard & Stewart 2011).

Earlier critics of AI-based theories of scientific discoveries argued that a computer cannot devise new concepts but is confined to the concepts included in the given computer language (Hempel 1985: 119–120). It cannot design new experiments, instruments, or methods. Subsequent computational research on scientific discovery was driven by the motivation to contribute computational tools to aid scientists in their research (Addis et al. 2016). It appears that computational methods can be used to generate new results leading to refereed scientific publications in astrophysics, cancer research, ecology, and other fields (Langley 2000). However, the philosophical discussion has continued about the question of whether these methods really generate new knowledge or whether they merely speed up data processing. It is also still an open question whether data-intensive science is fundamentally different from traditional research, for instance regarding the status of hypothesis or theory in data-intensive research (Pietsch 2015).

In the wake of recent developments in machine learning, some older discussions about automated discovery have been revived. The availability of vastly improved computational tools and software for data analysis has stimulated new discussions about computer-generated discovery (see Leonelli 2020). It is largely uncontroversial that machine learning tools can aid discovery, for instance in research on antibiotics (Stokes et al, 2020). The notion of “robot scientist” is mostly used metaphorically, and the vision that human scientists may one day be replaced by computers – by successors of the laboratory automation systems “Adam” and “Eve”, allegedly the first “robot scientists” – is evoked in writings for broader audiences (see King et al. 2009, Williams et al. 2015, for popularized descriptions of these systems), although some interesting ethical challenges do arise from “superhuman AI” (see Russell 2021). It also appears that, on the notion that products of creative acts are both novel and valuable, AI systems should be called “creative,” an implication which not all analysts will find plausible (Boden 2014)

Philosophical analyses focus on various questions arising from the processes involving human-machine complexes. One issue relevant to the problem of scientific discovery arises from the opacity of machine learning. If machine learning indeed escapes human understanding, how can we be warranted to say that knowledge or understanding is generated by deep learning tools? Might we have reason to say that humans and machines are “co-developers” of knowledge (Tamaddoni-Nezhad et al. 2021)?

New perspectives on scientific discovery have also opened up in the context of social epistemology (see Goldman & O’Connor 2021). Social epistemology investigates knowledge production as a group process, specifically the epistemic effects of group composition in terms of cognitive diversity and unity and social interactions within groups or institutions such as testimony and trust, peer disagreement and critique, and group justification, among others. On this view, discovery is a collective achievement, and the task is to explore how assorted social-epistemic activities or practices have an impact on the knowledge generated by groups in question. There are obvious implications for debates about scientific discovery of recent research in the different branches of social epistemology. Social epistemologists have examined individual cognitive agents in their roles as group members (as providers of information or as critics) and the interactions among these members (Longino 2001), groups as aggregates of diverse agents, or the entire group as epistemic agent (e.g., Koons 2021, Dragos 2019).

Standpoint theory, for instance, explores the role of outsiders in knowledge generation, considering how the sociocultural structures and practices in which individuals are embedded aid or obstruct the generation of creative ideas. According to standpoint theorists, people with standpoint are politically aware and politically engaged people outside the mainstream. Because people with standpoint have different experiences and access to different domains of expertise than most members of a culture, they can draw on rich conceptual resources for creative thinking (Solomon 2007).

Social epistemologists examining groups as aggregates of agents consider to what extent diversity among group members is conducive to knowledge production and whether and to what extent beliefs and attitudes must be shared among group members to make collective knowledge possible (Bird 2014). This is still an open question. Some formal approaches to model the influence of diversity on knowledge generation suggest that cognitive diversity is beneficial to collective knowledge generation (Weisberg and Muldoon 2009), but others have criticized the model (Alexander et al (2015), see also Thoma (2015) and Poyhönen (2017) for further discussion).

This essay has illustrated that philosophy of discovery has come full circle. Philosophy of discovery has once again become a thriving field of philosophical study, now intersecting with, and drawing on philosophical and empirical studies of creative thinking, problem solving under uncertainty, collective knowledge production, and machine learning. Recent approaches to discovery are typically explicitly interdisciplinary and integrative, cutting across previous distinctions among hypothesis generation and theory building, data collection, assessment, and selection; as well as descriptive-analytic, historical, and normative perspectives (Danks & Ippoliti 2018, Michel 2021). The goal no longer is to provide one overarching account of scientific discovery but to produce multifaceted analyses of past and present activities of knowledge generation in all their complexity and heterogeneity that are illuminating to the non-scientist and the scientific researcher alike.

• Abraham, A. 2019, The Neuroscience of Creativity, Cambridge: Cambridge University Press.
• Addis, M., Sozou, P.D., Gobet, F. and Lane, P. R., 2016, “Computational scientific discovery and cognitive science theories”, in Mueller, V. C. (ed.) Computing and Philosophy , Springer, 83–87.
• Alexander, J., Himmelreich, J., and Thompson, C. 2015, Epistemic Landscapes, Optimal Search, and the Division of Cognitive Labor, Philosophy of Science 82: 424–453.
• Arabatzis, T. 1996, “Rethinking the ‘Discovery’ of the Electron,” Studies in History and Philosophy of Science Part B Studies In History and Philosophy of Modern Physics , 27: 405–435.
• Bartha, P., 2010, By Parallel Reasoning: The Construction and Evaluation of Analogical Arguments , New York: Oxford University Press.
• Bechtel, W. and R. Richardson, 1993, Discovering Complexity , Princeton: Princeton University Press.
• Benjamin, A.C., 1934, “The Mystery of Scientific Discovery ” Philosophy of Science , 1: 224–36.
• Bird, A. 2014, “When is There a Group that Knows? Distributed Cognition, Scientific Knowledge, and the Social Epistemic Subject”, in J. Lackey (ed.), Essays in Collective Epistemology , Oxford: Oxford University Press, 42–63.
• Blackburn, S. 2014, “Creativity and Not-So-Dumb Luck”, in Paul, E. S. and Kaufman, S. B. (eds.), The Philosophy of Creativity: New Essays , New York: Oxford Academic online edn. https://doi.org/10.1093/acprof:oso/9780199836963.003.0008.
• Blackwell, R.J., 1969, Discovery in the Physical Sciences , Notre Dame: University of Notre Dame Press.
• Boden, M.A., 2004, The Creative Mind: Myths and Mechanisms , London: Routledge.
• –––, 2014, “Creativity and Artificial Intelligence: A Contradiction in Terms?”, in Paul, E. S. and Kaufman, S. B. (eds.), The Philosophy of Creativity: New Essays (New York: Oxford Academic online edn., https://doi.org/10.1093/acprof:oso/9780199836963.003.0012 .
• Brannigan, A., 1981, The Social Basis of Scientific Discoveries , Cambridge: Cambridge University Press.
• Brem, S. and L.J. Rips, 2000, “Explanation and Evidence in Informal Argument”, Cognitive Science , 24: 573–604.
• Campbell, D., 1960, “Blind Variation and Selective Retention in Creative Thought as in Other Knowledge Processes”, Psychological Review , 67: 380–400.
• Carmichael, R.D., 1922, “The Logic of Discovery”, The Monist , 32: 569–608.
• –––, 1930, The Logic of Discovery , Chicago: Open Court.
• Chow, S. 2015, “Many Meanings of ‘Heuristic’”, British Journal for the Philosophy of Science , 66: 977–1016
• Craver, C.F., 2002, “Interlevel Experiments, Multilevel Mechanisms in the Neuroscience of Memory”, Philosophy of Science Supplement , 69: 83–97.
• Craver, C.F. and L. Darden, 2013, In Search of Mechanisms: Discoveries across the Life Sciences , Chicago: University of Chicago Press.
• Curd, M., 1980, “The Logic of Discovery: An Analysis of Three Approaches”, in T. Nickles (ed.) Scientific Discovery, Logic, and Rationality , Dordrecht: D. Reidel, 201–19.
• Danks, D. & Ippoliti, E. (eds.) 2018, Building Theories: Heuristics and Hypotheses in Sciences , Cham: Springer.
• Darden, L., 1991, Theory Change in Science: Strategies from Mendelian Genetics , New York: Oxford University Press.
• –––, 2002, “Strategies for Discovering Mechanisms: Schema Instantiation, Modular Subassembly, Forward/Backward Chaining”, Philosophy of Science , 69: S354-S65.
• –––, 2009, “Discovering Mechanisms in Molecular Biology: Finding and Fixing Incompleteness and Incorrectness”, in J. Meheus and T. Nickles (eds.), Models of Discovery and Creativity , Dordrecht: Springer, 43–55.
• Dewey, J. 1910, How We Think . Boston: D.C. Heath
• Dragos, C., 2019, “Groups Can Know How” American Philosophical Quarterly 56: 265–276
• Ducasse, C.J., 1951, “Whewell’s Philosophy of Scientific Discovery II”, The Philosophical Review , 60(2): 213–34.
• Dunbar, K., 1997, “How scientists think: On-line creativity and conceptual change in science”, in T.B. Ward, S.M. Smith, and J. Vaid (eds.), Conceptual Structures and Processes: Emergence, Discovery, and Change , Washington, DC: American Psychological Association Press, 461–493.
• –––, 2001, “The Analogical Paradox: Why Analogy is so Easy in Naturalistic Settings Yet so Difficult in Psychological Laboratories”, in D. Gentner, K.J. Holyoak, and B.N. Kokinov (eds.), The Analogical Mind: Perspectives from Cognitive Science , Cambridge, MA: MIT Press.
• Dunbar, K, J. Fugelsang, and C Stein, 2007, “Do Naïve Theories Ever Go Away? Using Brain and Behavior to Understand Changes in Concepts”, in M. Lovett and P. Shah (eds.), Thinking with Data: 33rd Carnegie Symposium on Cognition , Mahwah: Erlbaum, 193–205.
• Feist, G.J., 1999, “The Influence of Personality on Artistic and Scientific Creativity”, in R.J. Sternberg (ed.), Handbook of Creativity , New York: Cambridge University Press, 273–96.
• –––, 2006, The psychology of science and the origins of the scientific mind , New Haven: Yale University Press.
• Gillies D., 1996, Artificial intelligence and scientific method . Oxford: Oxford University Press.
• –––, 2018 “Discovering Cures in Medicine” in Danks, D. & Ippoliti, E. (eds.), Building Theories: Heuristics and Hypotheses in Sciences , Cham: Springer, 83–100.
• Goldman, Alvin & O’Connor, C., 2021, “Social Epistemology”, The Stanford Encyclopedia of Philosophy (Winter 2021 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/win2021/entries/epistemology-social/>.
• Gramelsberger, G. 2011, “What Do Numerical (Climate) Models Really Represent?” Studies in History and Philosophy of Science 42: 296–302.
• Gutting, G., 1980, “Science as Discovery”, Revue internationale de philosophie , 131: 26–48.
• Hanson, N.R., 1958, Patterns of Discovery , Cambridge: Cambridge University Press.
• –––, 1960, “Is there a Logic of Scientific Discovery?”, Australasian Journal of Philosophy , 38: 91–106.
• –––, 1965, “Notes Toward a Logic of Discovery”, in R.J. Bernstein (ed.), Perspectives on Peirce. Critical Essays on Charles Sanders Peirce , New Haven and London: Yale University Press, 42–65.
• Harman, G.H., 1965, “The Inference to the Best Explanation”, Philosophical Review , 74.
• Hausman, C. R. 1984, A Discourse on Novelty and Creation , New York: SUNY Press.
• Hempel, C.G., 1985, “Thoughts in the Limitations of Discovery by Computer”, in K. Schaffner (ed.), Logic of Discovery and Diagnosis in Medicine , Berkeley: University of California Press, 115–22.
• Hesse, M., 1966, Models and Analogies in Science , Notre Dame: University of Notre Dame Press.
• Hey, S. 2016 “Heuristics and Meta-heuristics in Scientific Judgement”, British Journal for the Philosophy of Science , 67: 471–495
• Hills, A., Bird, A. 2019, “Against Creativity”, Philosophy and Phenomenological Research , 99: 694–713.
• Holyoak, K.J. and P. Thagard, 1996, Mental Leaps: Analogy in Creative Thought , Cambridge, MA: MIT Press.
• Howard, D., 2006, “Lost Wanderers in the Forest of Knowledge: Some Thoughts on the Discovery-Justification Distinction”, in J. Schickore and F. Steinle (eds.), Revisiting Discovery and Justification. Historical and Philosophical Perspectives on the Context Distinction , Dordrecht: Springer, 3–22.
• Hoyningen-Huene, P., 1987, “Context of Discovery and Context of Justification”, Studies in History and Philosophy of Science , 18: 501–15.
• Hull, D.L., 1988, Science as Practice: An Evolutionary Account of the Social and Conceptual Development of Science , Chicago: University of Chicago Press.
• Ippoliti, E. 2018, “Heuristic Logic. A Kernel” in Danks, D. & Ippoliti, E. (eds.) Building Theories: Heuristics and Hypotheses in Sciences , Cham: Springer, 191–212
• Jantzen, B.C., 2016, “Discovery without a ‘Logic’ would be a Miracle”, Synthese , 193: 3209–3238.
• Johnson-Laird, P., 1983, Mental Models , Cambridge: Cambridge University Press.
• Kieran, M., 2014, “Creativity as a Virtue of Character,” in E. Paul and S. B. Kaufman (eds.), The Philosophy of Creativity: New Essays . Oxford: Oxford University Press, 125–44
• King, R. D. et al. 2009, “The Automation of Science”, Science 324: 85–89.
• Koehler, D.J., 1991, “Explanation, Imagination, and Confidence in Judgment”, Psychological Bulletin , 110: 499–519.
• Koertge, N. 1980, “Analysis as a Method of Discovery during the Scientific Revolution” in Nickles, T. (ed.) Scientific Discovery, Logic, and Rationality vol. I, Dordrecht: Reidel, 139–157
• Koons, J.R. 2021, “Knowledge as a Collective Status”, Analytic Philosophy , https://doi.org/10.1111/phib.12224
• Kounios, J. and Beeman, M. 2009, “The Aha! Moment : The Cognitive Neuroscience of Insight”, Current Directions in Psychological Science , 18: 210–16.
• Kordig, C., 1978, “Discovery and Justification”, Philosophy of Science , 45: 110–17.
• Kronfeldner, M. 2009, “Creativity Naturalized”, The Philosophical Quarterly 59: 577–592.
• Kuhn, T.S., 1970 [1962], The Structure of Scientific Revolutions , 2 nd edition, Chicago: The University of Chicago Press; first edition, 1962.
• Kulkarni, D. and H.A. Simon, 1988, “The processes of scientific discovery: The strategy of experimentation”, Cognitive Science , 12: 139–76.
• Langley, P., 2000, “The Computational Support of Scientific Discovery”, International Journal of Human-Computer Studies , 53: 393–410.
• Langley, P., H.A. Simon, G.L. Bradshaw, and J.M. Zytkow, 1987, Scientific Discovery: Computational Explorations of the Creative Processes , Cambridge, MA: MIT Press.
• Laudan, L., 1980, “Why Was the Logic of Discovery Abandoned?” in T. Nickles (ed.), Scientific Discovery (Volume I), Dordrecht: D. Reidel, 173–83.
• Leonelli, S. 2020, “Scientific Research and Big Data”, The Stanford Encyclopedia of Philosophy (Summer 2020 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/sum2020/entries/science-big-data/>
• Leplin, J., 1987, “The Bearing of Discovery on Justification”, Canadian Journal of Philosophy , 17: 805–14.
• Longino, H. 2001, The Fate of Knowledge , Princeton: Princeton University Press
• Lugg, A., 1985, “The Process of Discovery”, Philosophy of Science , 52: 207–20.
• Magnani, L., 2000, Abduction, Reason, and Science: Processes of Discovery and Explanation , Dordrecht: Kluwer.
• –––, 2009, “Creative Abduction and Hypothesis Withdrawal”, in J. Meheus and T. Nickles (eds.), Models of Discovery and Creativity , Dordrecht: Springer.
• Magnani, L. and N.J. Nersessian, 2002, Model-Based Reasoning: Science, Technology, and Values , Dordrecht: Kluwer.
• Magnani, L., N.J. Nersessian, and P. Thagard, 1999, Model-Based Reasoning in Scientific Discovery , Dordrecht: Kluwer.
• Michel, J. (ed.) 2021, Making Scientific Discoveries. Interdisciplinary Reflections , Brill | mentis.
• Minai, A., Doboli, S., Iyer, L. 2022 “Models of Creativity and Ideation: An Overview” in Ali A. Minai, Jared B. Kenworthy, Paul B. Paulus, Simona Doboli (eds.), Creativity and Innovation. Cognitive, Social, and Computational Approaches , Springer, 21–46.
• Nersessian, N.J., 1992, “How do scientists think? Capturing the dynamics of conceptual change in science”, in R. Giere (ed.), Cognitive Models of Science , Minneapolis: University of Minnesota Press, 3–45.
• –––, 1999, “Model-based reasoning in conceptual change”, in L. Magnani, N.J. Nersessian and P. Thagard (eds.), Model-Based Reasoning in Scientific Discovery , New York: Kluwer, 5–22.
• –––, 2009, “Conceptual Change: Creativity, Cognition, and Culture ” in J. Meheus and T. Nickles (eds.), Models of Discovery and Creativity , Dordrecht: Springer, 127–66.
• Newell, A. and H. A Simon, 1971, “Human Problem Solving: The State of the Theory in 1970”, American Psychologist , 26: 145–59.
• Newton, I. 1718, Opticks; or, A Treatise of the Reflections, Inflections and Colours of Light , London: Printed for W. and J. Innys, Printers to the Royal Society.
• Nickles, T., 1984, “Positive Science and Discoverability”, PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association , 1984: 13–27.
• –––, 1985, “Beyond Divorce: Current Status of the Discovery Debate”, Philosophy of Science , 52: 177–206.
• –––, 1989, “Truth or Consequences? Generative versus Consequential Justification in Science”, PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association , 1988, 393–405.
• –––, 2018, “TTT: A Fast Heuristic to New Theories?” in Danks, D. & Ippoliti, E. (eds.) Building Theories: Heuristics and Hypotheses in Sciences , Cham: Springer, 213–244.
• Pasquale, J.-F. de and Poirier, P. 2016, “Convolution and Modal Representations in Thagard and Stewart’s Neural Theory of Creativity: A Critical Analysis ”, Synthese , 193: 1535–1560
• Paul, E. S. and Kaufman, S. B. (eds.), 2014a, The Philosophy of Creativity: New Essays , New York: Oxford Academic online edn., https://doi.org/10.1093/acprof:oso/9780199836963.001.0001.
• –––, 2014b, “Introducing: The Philosophy of Creativity”, in Paul, E. S. and Kaufman, S. B. (eds.), The Philosophy of Creativity: New Essays (New York: Oxford Academic online edn., https://doi.org/10.1093/acprof:oso/9780199836963.003.0001.
• Pietsch, W. 2015, “Aspects of Theory-Ladenness in Data-Intensive Science”, Philosophy of Science 82: 905–916.
• Popper, K., 2002 [1934/1959], The Logic of Scientific Discovery , London and New York: Routledge; original published in German in 1934; first English translation in 1959.
• Pöyhönen, S. 2017, “Value of Cognitive Diversity in Science”, Synthese , 194(11): 4519–4540. doi:10.1007/s11229–016-1147-4
• Pulte, H. 2019, “‘‘Tis Much Better to Do a Little with Certainty’: On the Reception of Newton’s Methodology”, in The Reception of Isaac Newton in Europe , Pulte, H, and Mandelbrote, S. (eds.), Continuum Publishing Corporation, 355–84.
• Reichenbach, H., 1938, Experience and Prediction. An Analysis of the Foundations and the Structure of Knowledge , Chicago: The University of Chicago Press.
• Richardson, A., 2006, “Freedom in a Scientific Society: Reading the Context of Reichenbach’s Contexts”, in J. Schickore and F. Steinle (eds.), Revisiting Discovery and Justification. Historical and Philosophical Perspectives on the Context Distinction , Dordrecht: Springer, 41–54.
• Russell, S. 2021, “Human-Compatible Artificial Intelligence”, in Human Like Machine Intelligence , Muggleton, S. and Charter, N. (eds.), Oxford: Oxford University Press, 4–23
• Schaffer, S., 1986, “Scientific Discoveries and the End of Natural Philosophy”, Social Studies of Science , 16: 387–420.
• –––, 1994, “Making Up Discovery”, in M.A. Boden (ed.), Dimensions of Creativity , Cambridge, MA: MIT Press, 13–51.
• Schaffner, K., 1993, Discovery and Explanation in Biology and Medicine , Chicago: University of Chicago Press.
• –––, 2008 “Theories, Models, and Equations in Biology: The Heuristic Search for Emergent Simplifications in Neurobiology”, Philosophy of Science , 75: 1008–21.
• Schickore, J. and F. Steinle, 2006, Revisiting Discovery and Justification. Historical and Philosophical Perspectives on the Context Distinction , Dordrecht: Springer.
• Schiller, F.C.S., 1917, “Scientific Discovery and Logical Proof”, in C.J. Singer (ed.), Studies in the History and Method of Science (Volume 1), Oxford: Clarendon, 235–89.
• Simon, H.A., 1973, “Does Scientific Discovery Have a Logic?”, Philosophy of Science , 40: 471–80.
• –––, 1977, Models of Discovery and Other Topics in the Methods of Science , Dordrecht: D. Reidel.
• Simon, H.A., P.W. Langley, and G.L. Bradshaw, 1981, “Scientific Discovery as Problem Solving”, Synthese , 47: 1–28.
• Smith, G.E., 2002, “The Methodology of the Principia ”, in G.E. Smith and I.B. Cohen (eds), The Cambridge Companion to Newton , Cambridge: Cambridge University Press, 138–73.
• Simonton, D. K., “Hierarchies of Creative Domains: Disciplinary Constraints on Blind Variation and Selective Retention”, in Paul, E. S. and Kaufman, S. B. (eds), The Philosophy of Creativity: New Essays , New York: Oxford Academic online edn. https://doi.org/10.1093/acprof:oso/9780199836963.003.0013
• Snyder, L.J., 1997, “Discoverers’ Induction”, Philosophy of Science , 64: 580–604.
• Solomon, M., 2009, “Standpoint and Creativity”, Hypatia : 226–37.
• Sternberg, R J. and T. I. Lubart, 1999, “The concept of creativity: Prospects and paradigms,” in R. J. Sternberg (ed.) Handbook of Creativity , Cambridge: Cambridge University Press, 3–15.
• Stokes, D., 2011, “Minimally Creative Thought”, Metaphilosophy , 42: 658–81.
• Tamaddoni-Nezhad, A., Bohan, D., Afroozi Milani, G., Raybould, A., Muggleton, S., 2021, “Human–Machine Scientific Discovery”, in Human Like Machine Intelligence , Muggleton, S. and Charter, N., (eds.), Oxford: Oxford University Press, 297–315
• Thagard, P., 1984, “Conceptual Combination and Scientific Discovery”, PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association , 1984(1): 3–12.
• –––, 1999, How Scientists Explain Disease , Princeton: Princeton University Press.
• –––, 2010, “How Brains Make Mental Models”, in L. Magnani, N.J. Nersessian and P. Thagard (eds.), Model-Based Reasoning in Science & Technology , Berlin and Heidelberg: Springer, 447–61.
• –––, 2012, The Cognitive Science of Science , Cambridge, MA: MIT Press.
• Thagard, P. and Stewart, T. C., 2011, “The AHA! Experience: Creativity Through Emergent Binding in Neural Networks”, Cognitive Science , 35: 1–33.
• Thoma, Johanna, 2015, “The Epistemic Division of Labor Revisited”, Philosophy of Science , 82: 454–472. doi:10.1086/681768
• Weber, M., 2005, Philosophy of Experimental Biology , Cambridge: Cambridge University Press.
• Whewell, W., 1996 [1840], The Philosophy of the Inductive Sciences (Volume II), London: Routledge/Thoemmes.
• Weisberg, M. and Muldoon, R., 2009, “Epistemic Landscapes and the Division of Cognitive Labor”, Philosophy of Science , 76: 225–252. doi:10.1086/644786
• Williams, K. et al. 2015, “Cheaper Faster Drug Development Validated by the Repositioning of Drugs against Neglected Tropical Diseases”, Journal of the Royal Society Interface 12: 20141289. http://dx.doi.org/10.1098/rsif.2014.1289.
• Zahar, E., 1983, “Logic of Discovery or Psychology of Invention?”, British Journal for the Philosophy of Science , 34: 243–61.
• Zednik, C. and Jäkel, F. 2016 “Bayesian Reverse-Engineering Considered as a Research Strategy for Cognitive Science”, Synthese , 193, 3951–3985.

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How to write an introduction section of a scientific article?

An article primarily includes the following sections: introduction, materials and methods, results, discussion, and conclusion. Before writing the introduction, the main steps, the heading and the familiarity level of the readers should be considered. Writing should begin when the experimental system and the equipment are available. The introduction section comprises the first portion of the manuscript, and it should be written using the simple present tense. Additionally, abbreviations and explanations are included in this section. The main goal of the introduction is to convey basic information to the readers without obligating them to investigate previous publications and to provide clues as to the results of the present study. To do this, the subject of the article should be thoroughly reviewed, and the aim of the study should be clearly stated immediately after discussing the basic references. In this review, we aim to convey the principles of writing the introduction section of a manuscript to residents and young investigators who have just begun to write a manuscript.

Introduction

When entering a gate of a magnificent city we can make a prediction about the splendor, pomposity, history, and civilization we will encounter in the city. Occasionally, gates do not give even a glimpse of the city, and it can mislead the visitors about inner sections of the city. Introduction sections of the articles are like gates of a city. It is a presentation aiming at introducing itself to the readers, and attracting their attention. Attractiveness, clarity, piquancy, and analytical capacity of the presentation will urge the reader to read the subsequent sections of the article. On the other hand as is understood from the motto of antique Greek poet Euripides “a bad beginning makes a bad ending”, ‘Introduction’ section of a scientific article is important in that it can reveal the conclusion of the article. [ 1 ]

It is useful to analyze the issues to be considered in the ‘Introduction’ section under 3 headings. Firstly, information should be provided about the general topic of the article in the light of the current literature which paves the way for the disclosure of the objective of the manuscript. Then the specific subject matter, and the issue to be focused on should be dealt with, the problem should be brought forth, and fundamental references related to the topic should be discussed. Finally, our recommendations for solution should be described, in other words our aim should be communicated. When these steps are followed in that order, the reader can track the problem, and its solution from his/her own perspective under the light of current literature. Otherwise, even a perfect study presented in a non-systematized, confused design will lose the chance of reading. Indeed inadequate information, inability to clarify the problem, and sometimes concealing the solution will keep the reader who has a desire to attain new information away from reading the manuscript. [ 1 – 3 ]

First of all, explanation of the topic in the light of the current literature should be made in clear, and precise terms as if the reader is completely ignorant of the subject. In this section, establishment of a warm rapport between the reader, and the manuscript is aimed. Since frantic plunging into the problem or the solution will push the reader into the dilemma of either screening the literature about the subject matter or refraining from reading the article. Updated, and robust information should be presented in the ‘Introduction’ section.

Then main topic of our manuscript, and the encountered problem should be analyzed in the light of the current literature following a short instance of brain exercise. At this point the problems should be reduced to one issue as far as possible. Of course, there might be more than one problem, however this new issue, and its solution should be the subject matter of another article. Problems should be expressed clearly. If targets are more numerous, and complex, solutions will be more than one, and confusing.

Finally, the last paragraphs of the ‘Introduction’ section should include the solution in which we will describe the information we generated, and related data. Our sentences which arouse curiosity in the readers should not be left unanswered. The reader who thinks to obtain the most effective information in no time while reading a scientific article should not be smothered with mysterious sentences, and word plays, and the readers should not be left alone to arrive at a conclusion by themselves. If we have contrary expectations, then we might write an article which won’t have any reader. A clearly expressed or recommended solutions to an explicitly revealed problem is also very important for the integrity of the ‘Introduction’ section. [ 1 – 5 ]

We can summarize our arguments with the following example ( Figure 1 ). The introduction section of the exemplary article is written in simple present tense which includes abbreviations, acronyms, and their explanations. Based on our statements above we can divide the introduction section into 3 parts. In the first paragraph, miniaturization, and evolvement of pediatric endourological instruments, and competitions among PNL, ESWL, and URS in the treatment of urinary system stone disease are described, in other words the background is prepared. In the second paragraph, a newly defined system which facilitates intrarenal access in PNL procedure has been described. Besides basic references related to the subject matter have been given, and their outcomes have been indicated. In other words, fundamental references concerning main subject have been discussed. In the last paragraph the aim of the researchers to investigate the outcomes, and safety of the application of this new method in the light of current information has been indicated.

An exemplary introduction section of an article

Apart from the abovementioned information about the introduction section of a scientific article we will summarize a few major issues in brief headings

Important points which one should take heed of:

• Abbreviations should be given following their explanations in the ‘Introduction’ section (their explanations in the summary does not count)
• Simple present tense should be used.
• References should be selected from updated publication with a higher impact factor, and prestigous source books.
• Avoid mysterious, and confounding expressions, construct clear sentences aiming at problematic issues, and their solutions.
• The sentences should be attractive, tempting, and comjprehensible.
• Firstly general, then subject-specific information should be given. Finally our aim should be clearly explained.

How to Write the Introduction to a Scientific Paper?

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An Introduction to a scientific paper familiarizes the reader with the background of the issue at hand. It must reflect why the issue is topical and its current importance in the vast sea of research being done globally. It lays the foundation of biomedical writing and is the first portion of an article according to the IMRAD pattern ( I ntroduction, M ethodology, R esults, a nd D iscussion) [1].

I once had a professor tell a class that he sifted through our pile of essays, glancing at the titles and introductions, looking for something that grabbed his attention. Everything else went to the bottom of the pile to be read last, when he was tired and probably grumpy from all the marking. Don’t get put at the bottom of the pile, he said. Anonymous

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The Introduction Section

Abstract and Keywords

Writing the Abstract

1 what is the importance of an introduction.

An Introduction to a scientific paper familiarizes the reader with the background of the issue at hand. It must reflect why the issue is topical and its current importance in the vast sea of research being done globally. It lays the foundation of biomedical writing and is the first portion of an article according to the IMRAD pattern ( I ntroduction, M ethodology, R esults, a nd D iscussion) [ 1 ].

It provides the flavour of the article and many authors have used phrases to describe it for example—'like a gate of the city’ [ 2 ], ‘the beginning is half of the whole’ [ 3 ], ‘an introduction is not just wrestling with words to fit the facts, but it also strongly modulated by perception of the anticipated reactions of peer colleagues’, [ 4 ] and ‘an introduction is like the trailer to a movie’. A good introduction helps captivate the reader early.

2 What Are the Principles of Writing a Good Introduction?

A good introduction will ‘sell’ an article to a journal editor, reviewer, and finally to a reader [ 3 ]. It should contain the following information [ 5 , 6 ]:

The known—The background scientific data

The unknown—Gaps in the current knowledge

Research hypothesis or question

Methodologies used for the study

The known consist of citations from a review of the literature whereas the unknown is the new work to be undertaken. This part should address how your work is the required missing piece of the puzzle.

3 What Are the Models of Writing an Introduction?

The Problem-solving model

First described by Swales et al. in 1979, in this model the writer should identify the ‘problem’ in the research, address the ‘solution’ and also write about ‘the criteria for evaluating the problem’ [ 7 , 8 ].

The CARS model that stands for C reating A R esearch S pace [ 9 , 10 ].

The two important components of this model are:

Establishing a territory (situation)

Establishing a niche (problem)

Occupying a niche (the solution)

In this popular model, one can add a fourth point, i.e., a conclusion [ 10 ].

4 What Is Establishing a Territory?

This includes: [ 9 ]

Stating the general topic and providing some background about it.

Providing a brief and relevant review of the literature related to the topic.

Adding a paragraph on the scope of the topic including the need for your study.

5 What Is Establishing a Niche?

Establishing a niche includes:

Stating the importance of the problem.

Outlining the current situation regarding the problem citing both global and national data.

Evaluating the current situation (advantages/ disadvantages).

Identifying the gaps.

Emphasizing the importance of the proposed research and how the gaps will be addressed.

Stating the research problem/ questions.

Stating the hypotheses briefly.

Figure 17.1 depicts how the introduction needs to be written. A scientific paper should have an introduction in the form of an inverted pyramid. The writer should start with the general information about the topic and subsequently narrow it down to the specific topic-related introduction.

Flow of ideas from the general to the specific

6 What Does Occupying a Niche Mean?

This is the third portion of the introduction and defines the rationale of the research and states the research question. If this is missing the reviewers will not understand the logic for publication and is a common reason for rejection [ 11 , 12 ]. An example of this is given below:

Till date, no study has been done to see the effectiveness of a mesh alone or the effectiveness of double suturing along with a mesh in the closure of an umbilical hernia regarding the incidence of failure. So, the present study is aimed at comparing the effectiveness of a mesh alone versus the double suturing technique along with a mesh.

7 How Long Should the Introduction Be?

For a project protocol, the introduction should be about 1–2 pages long and for a thesis it should be 3–5 pages in a double-spaced typed setting. For a scientific paper it should be less than 10–15% of the total length of the manuscript [ 13 , 14 ].

8 How Many References Should an Introduction Have?

All sections in a scientific manuscript except the conclusion should contain references. It has been suggested that an introduction should have four or five or at the most one-third of the references in the whole paper [ 15 ].

9 What Are the Important Points Which Should be not Missed in an Introduction?

An introduction paves the way forward for the subsequent sections of the article. Frequently well-planned studies are rejected by journals during review because of the simple reason that the authors failed to clarify the data in this section to justify the study [ 16 , 17 ]. Thus, the existing gap in knowledge should be clearly brought out in this section (Fig. 17.2 ).

How should the abstract, introduction, and discussion look

The following points are important to consider:

The introduction should be written in simple sentences and in the present tense.

Many of the terms will be introduced in this section for the first time and these will require abbreviations to be used later.

The references in this section should be to papers published in quality journals (e.g., having a high impact factor).

The aims, problems, and hypotheses should be clearly mentioned.

Start with a generalization on the topic and go on to specific information relevant to your research.

10 Example of an Introduction

11 Conclusions

An Introduction is a brief account of what the study is about. It should be short, crisp, and complete.

It has to move from a general to a specific research topic and must include the need for the present study.

The Introduction should include data from a literature search, i.e., what is already known about this subject and progress to what we hope to add to this knowledge.

Moore A. What’s in a discussion section? Exploiting 2-dimensionality in the online world. Bioassays. 2016;38(12):1185.

Article   Google Scholar  

Annesley TM. The discussion section: your closing argument. Clin Chem. 2010;56(11):1671–4.

Article   CAS   Google Scholar  

Bavdekar SB. Writing the discussion section: describing the significance of the study findings. J Assoc Physicians India. 2015;63(11):40–2.

PubMed   Google Scholar  

Foote M. The proof of the pudding: how to report results and write a good discussion. Chest. 2009;135(3):866–8.

Kearney MH. The discussion section tells us where we are. Res Nurs Health. 2017;40(4):289–91.

Ghasemi A, Bahadoran Z, Mirmiran P, Hosseinpanah F, Shiva N, Zadeh-Vakili A. The principles of biomedical scientific writing: discussion. Int J Endocrinol Metab. 2019;17(3):e95415.

Swales JM, Feak CB. Academic writing for graduate students: essential tasks and skills. Ann Arbor, MI: University of Michigan Press; 2004.

Google Scholar  

Colombo M, Bucher L, Sprenger J. Determinants of judgments of explanatory power: credibility, generality, and statistical relevance. Front Psychol. 2017;8:1430.

Mozayan MR, Allami H, Fazilatfar AM. Metadiscourse features in medical research articles: subdisciplinary and paradigmatic influences in English and Persian. Res Appl Ling. 2018;9(1):83–104.

Hyland K. Metadiscourse: mapping interactions in academic writing. Nordic J English Stud. 2010;9(2):125.

Hill AB. The environment and disease: association or causation? Proc Royal Soc Med. 2016;58(5):295–300.

Alpert JS. Practicing medicine in Plato’s cave. Am J Med. 2006;119(6):455–6.

Walsh K. Discussing discursive discussions. Med Educ. 2016;50(12):1269–70.

Polit DF, Beck CT. Generalization in quantitative and qualitative research: myths and strategies. Int J Nurs Stud. 2010;47(11):1451–8.

Jawaid SA, Jawaid M. How to write introduction and discussion. Saudi J Anaesth. 2019;13(Suppl 1):S18–9.

Jawaid SA, Baig M. How to write an original article. In: Jawaid SA, Jawaid M, editors. Scientific writing: a guide to the art of medical writing and scientific publishing. Karachi: Published by Med-Print Services; 2018. p. 135–50.

Hall GM, editor. How to write a paper. London: BMJ Books, BMJ Publishing Group; 2003. Structure of a scientific paper. p. 1–5.

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Nundy, S., Kakar, A., Bhutta, Z.A. (2022). How to Write the Introduction to a Scientific Paper?. In: How to Practice Academic Medicine and Publish from Developing Countries?. Springer, Singapore. https://doi.org/10.1007/978-981-16-5248-6_17

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