scientific meaning of essay

Write Like a Scientist

A Guide to Scientific Communication

What is scientific writing ?

Scientific writing is a technical form of writing that is designed to communicate scientific information to other scientists. Depending on the specific scientific genre—a journal article, a scientific poster, or a research proposal, for example—some aspects of the writing may change, such as its  purpose , audience , or organization . Many aspects of scientific writing, however, vary little across these writing genres. Important hallmarks of all scientific writing are summarized below. Genre-specific information is located  here  and under the “By Genre” tab at the top of the page.

What are some important hallmarks of professional scientific writing?

1. Its primary audience is other scientists. Because of its intended audience, student-oriented or general-audience details, definitions, and explanations — which are often necessary in lab manuals or reports — are not terribly useful. Explaining general-knowledge concepts or how routine procedures were performed actually tends to obstruct clarity, make the writing wordy, and detract from its professional tone.

2. It is concise and precise . A goal of scientific writing is to communicate scientific information clearly and concisely. Flowery, ambiguous, wordy, and redundant language run counter to the purpose of the writing.

3. It must be set within the context of other published work. Because science builds on and corrects itself over time, scientific writing must be situated in and  reference the findings of previous work . This context serves variously as motivation for new work being proposed or the paper being written, as points of departure or congruence for new findings and interpretations, and as evidence of the authors’ knowledge and expertise in the field.

All of the information under “The Essentials” tab is intended to help you to build your knowledge and skills as a scientific writer regardless of the scientific discipline you are studying or the specific assignment you might be working on. In addition to discussions of audience and purpose , professional conventions like conciseness and specificity, and how to find and use literature references appropriately, we also provide guidelines for how to organize your writing and how to avoid some common mechanical errors .

If you’re new to this site or to professional scientific writing, we recommend navigating the sub-sections under “The Essentials” tab in the order they’re provided. Once you’ve covered these essentials, you might find information on  genre-  or discipline-specific writing useful.

How to Write a Scientific Essay

When writing any essay it’s important to always keep the end goal in mind. You want to produce a document that is detailed, factual, about the subject matter and most importantly to the point.

Writing scientific essays will always be slightly different to when you write an essay for say English Literature . You need to be more analytical and precise when answering your questions. To help achieve this, you need to keep three golden rules in mind.

• Analysing the question, so that you know exactly what you have to do

Planning your answer

• Writing the essay

Now, let’s look at these steps in more detail to help you fully understand how to apply the three golden rules.

Analysing the question

• Start by looking at the instruction. Essays need to be written out in continuous prose. You shouldn’t be using bullet points or writing in note form.
• If it helps to make a particular point, however, you can use a diagram providing it is relevant and adequately explained.
• Look at the topic you are required to write about. The wording of the essay title tells you what you should confine your answer to – there is no place for interesting facts about other areas.

The next step is to plan your answer. What we are going to try to do is show you how to produce an effective plan in a very short time. You need a framework to show your knowledge otherwise it is too easy to concentrate on only a few aspects.

For example, when writing an essay on biology we can divide the topic up in a number of different ways. So, if you have to answer a question like ‘Outline the main properties of life and system reproduction’

The steps for planning are simple. Firstly, define the main terms within the question that need to be addressed. Then list the properties asked for and lastly, roughly assess how many words of your word count you are going to allocate to each term.

Writing the Essay

The final step (you’re almost there), now you have your plan in place for the essay, it’s time to get it all down in black and white. Follow your plan for answering the question, making sure you stick to the word count, check your spelling and grammar and give credit where credit’s (always reference your sources).

How Tutors Breakdown Essays

An exceptional essay

• reflects the detail that could be expected from a comprehensive knowledge and understanding of relevant parts of the specification
• is free from fundamental errors
• maintains appropriate depth and accuracy throughout
• includes two or more paragraphs of material that indicates greater depth or breadth of study

A good essay

An average essay

• contains a significant amount of material that reflects the detail that could be expected from a knowledge and understanding of relevant parts of the specification.

In practice this will amount to about half the essay.

• is likely to reflect limited knowledge of some areas and to be patchy in quality
• demonstrates a good understanding of basic principles with some errors and evidence of misunderstanding

A poor essay

• contains much material which is below the level expected of a candidate who has completed the course
• Contains fundamental errors reflecting a poor grasp of basic principles and concepts

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Effective Writing

To construct sentences that reflect your ideas, focus these sentences appropriately. Express one idea per sentence. Use your current topic — that is, what you are writing about — as the grammatical subject of your sentence (see Verbs: Choosing between active and passive voice ). When writing a complex sentence (a sentence that includes several clauses), place the main idea in the main clause rather than a subordinate clause. In particular, focus on the phenomenon at hand, not on the fact that you observed it.

Constructing your sentences logically is a good start, but it may not be enough. To ensure they are readable, make sure your sentences do not tax readers' short-term memory by obliging these readers to remember long pieces of text before knowing what to do with them. In other words, keep together what goes together. Then, work on conciseness: See whether you can replace long phrases with shorter ones or eliminate words without loss of clarity or accuracy.

The following screens cover the drafting process in more detail. Specifically, they discuss how to use verbs effectively and how to take care of your text's mechanics.

Shutterstock. Much of the strength of a clause comes from its verb. Therefore, to express your ideas accurately, choose an appropriate verb and use it well. In particular, use it in the right tense, choose carefully between active and passive voice, and avoid dangling verb forms.

Verbs are for describing actions, states, or occurrences. To give a clause its full strength and keep it short, do not bury the action, state, or occurrence in a noun (typically combined with a weak verb), as in "The catalyst produced a significant increase in conversion rate." Instead write, "The catalyst increased the conversion rate significantly." The examples below show how an action, state, or occurrence can be moved from a noun back to a verb.

Using the right tense

In your scientific paper, use verb tenses (past, present, and future) exactly as you would in ordinary writing. Use the past tense to report what happened in the past: what you did, what someone reported, what happened in an experiment, and so on. Use the present tense to express general truths, such as conclusions (drawn by you or by others) and atemporal facts (including information about what the paper does or covers). Reserve the future tense for perspectives: what you will do in the coming months or years. Typically, most of your sentences will be in the past tense, some will be in the present tense, and very few, if any, will be in the future tense.

Work done We collected blood samples from . . . Groves et al. determined the growth rate of . . . Consequently, astronomers decided to rename . . . Work reported Jankowsky reported a similar growth rate . . . In 2009, Chu published an alternative method to . . . Irarrázaval observed the opposite behavior in . . . Observations The mice in Group A developed , on average, twice as much . . . The number of defects increased sharply . . . The conversion rate was close to 95% . . .

Present tense

General truths Microbes in the human gut have a profound influence on . . . The Reynolds number provides a measure of . . . Smoking increases the risk of coronary heart disease . . . Atemporal facts This paper presents the results of . . . Section 3.1 explains the difference between . . . Behbood's 1969 paper provides a framework for . . .

Future tense

Perspectives In a follow-up experiment, we will study the role of . . . The influence of temperature will be the object of future research . . .

Note the difference in scope between a statement in the past tense and the same statement in the present tense: "The temperature increased linearly over time" refers to a specific experiment, whereas "The temperature increases linearly over time" generalizes the experimental observation, suggesting that the temperature always increases linearly over time in such circumstances.

In complex sentences, you may have to combine two different tenses — for example, "In 1905, Albert Einstein postulated that the speed of light is constant . . . . " In this sentence, postulated refers to something that happened in the past (in 1905) and is therefore in the past tense, whereas is expresses a general truth and is in the present tense.

Choosing between active and passive voice

In English, verbs can express an action in one of two voices. The active voice focuses on the agent: "John measured the temperature." (Here, the agent — John — is the grammatical subject of the sentence.) In contrast, the passive voice focuses on the object that is acted upon: "The temperature was measured by John." (Here, the temperature, not John, is the grammatical subject of the sentence.)

To choose between active and passive voice, consider above all what you are discussing (your topic) and place it in the subject position. For example, should you write "The preprocessor sorts the two arrays" or "The two arrays are sorted by the preprocessor"? If you are discussing the preprocessor, the first sentence is the better option. In contrast, if you are discussing the arrays, the second sentence is better. If you are unsure what you are discussing, consider the surrounding sentences: Are they about the preprocessor or the two arrays?

The desire to be objective in scientific writing has led to an overuse of the passive voice, often accompanied by the exclusion of agents: "The temperature was measured " (with the verb at the end of the sentence). Admittedly, the agent is often irrelevant: No matter who measured the temperature, we would expect its value to be the same. However, a systematic preference for the passive voice is by no means optimal, for at least two reasons.

For one, sentences written in the passive voice are often less interesting or more difficult to read than those written in the active voice. A verb in the active voice does not require a person as the agent; an inanimate object is often appropriate. For example, the rather uninteresting sentence "The temperature was measured . . . " may be replaced by the more interesting "The measured temperature of 253°C suggests a secondary reaction in . . . ." In the second sentence, the subject is still temperature (so the focus remains the same), but the verb suggests is in the active voice. Similarly, the hard-to-read sentence "In this section, a discussion of the influence of the recirculating-water temperature on the conversion rate of . . . is presented " (long subject, verb at the end) can be turned into "This section discusses the influence of . . . . " The subject is now section , which is what this sentence is really about, yet the focus on the discussion has been maintained through the active-voice verb discusses .

As a second argument against a systematic preference for the passive voice, readers sometimes need people to be mentioned. A sentence such as "The temperature is believed to be the cause for . . . " is ambiguous. Readers will want to know who believes this — the authors of the paper, or the scientific community as a whole? To clarify the sentence, use the active voice and set the appropriate people as the subject, in either the third or the first person, as in the examples below.

Biologists believe the temperature to be . . . Keustermans et al. (1997) believe the temperature to be . . . The authors believe the temperature to be . . . We believe the temperature to be . . .

Avoiding dangling verb forms

A verb form needs a subject, either expressed or implied. When the verb is in a non-finite form, such as an infinitive ( to do ) or a participle ( doing ), its subject is implied to be the subject of the clause, or sometimes the closest noun phrase. In such cases, construct your sentences carefully to avoid suggesting nonsense. Consider the following two examples.

To dissect its brain, the affected fly was mounted on a . . . After aging for 72 hours at 50°C, we observed a shift in . . .

Here, the first sentence implies that the affected fly dissected its own brain, and the second implies that the authors of the paper needed to age for 72 hours at 50°C in order to observe the shift. To restore the intended meaning while keeping the infinitive to dissect or the participle aging , change the subject of each sentence as appropriate:

To dissect its brain, we mounted the affected fly on a . . . After aging for 72 hours at 50°C, the samples exhibited a shift in . . .

Alternatively, you can change or remove the infinitive or participle to restore the intended meaning:

To have its brain dissected , the affected fly was mounted on a . . . After the samples aged for 72 hours at 50°C, we observed a shift in . . .

In communication, every detail counts. Although your focus should be on conveying your message through an appropriate structure at all levels, you should also save some time to attend to the more mechanical aspects of writing in English, such as using abbreviations, writing numbers, capitalizing words, using hyphens when needed, and punctuating your text correctly.

Using abbreviations

Beware of overusing abbreviations, especially acronyms — such as GNP for gold nanoparticles . Abbreviations help keep a text concise, but they can also render it cryptic. Many acronyms also have several possible extensions ( GNP also stands for gross national product ).

Write acronyms (and only acronyms) in all uppercase ( GNP , not gnp ).

Introduce acronyms systematically the first time they are used in a document. First write the full expression, then provide the acronym in parentheses. In the full expression, and unless the journal to which you submit your paper uses a different convention, capitalize the letters that form the acronym: "we prepared Gold NanoParticles (GNP) by . . . " These capitals help readers quickly recognize what the acronym designates.

• Do not use capitals in the full expression when you are not introducing an acronym: "we prepared gold nanoparticles by… "
• As a more general rule, use first what readers know or can understand best, then put in parentheses what may be new to them. If the acronym is better known than the full expression, as may be the case for techniques such as SEM or projects such as FALCON, consider placing the acronym first: "The FALCON (Fission-Activated Laser Concept) program at…"
• In the rare case that an acronym is commonly known, you might not need to introduce it. One example is DNA in the life sciences. When in doubt, however, introduce the acronym.

In papers, consider the abstract as a stand-alone document. Therefore, if you use an acronym in both the abstract and the corresponding full paper, introduce that acronym twice: the first time you use it in the abstract and the first time you use it in the full paper. However, if you find that you use an acronym only once or twice after introducing it in your abstract, the benefit of it is limited — consider avoiding the acronym and using the full expression each time (unless you think some readers know the acronym better than the full expression).

Writing numbers

In general, write single-digit numbers (zero to nine) in words, as in three hours , and multidigit numbers (10 and above) in numerals, as in 24 hours . This rule has many exceptions, but most of them are reasonably intuitive, as shown hereafter.

Use numerals for numbers from zero to nine

• when using them with abbreviated units ( 3 mV );
• in dates and times ( 3 October , 3 pm );
• to identify figures and other items ( Figure 3 );
• for consistency when these numbers are mixed with larger numbers ( series of 3, 7, and 24 experiments ).

Use words for numbers above 10 if these numbers come at the beginning of a sentence or heading ("Two thousand eight was a challenging year for . . . "). As an alternative, rephrase the sentence to avoid this issue altogether ("The year 2008 was challenging for . . . " ) .

Capitalizing words

Capitals are often overused. In English, use initial capitals

• at beginnings: the start of a sentence, of a heading, etc.;
• for proper nouns, including nouns describing groups (compare physics and the Physics Department );
• for items identified by their number (compare in the next figure and in Figure 2 ), unless the journal to which you submit your paper uses a different convention;
• for specific words: names of days ( Monday ) and months ( April ), adjectives of nationality ( Algerian ), etc.

In contrast, do not use initial capitals for common nouns: Resist the temptation to glorify a concept, technique, or compound with capitals. For example, write finite-element method (not Finite-Element Method ), mass spectrometry (not Mass Spectrometry ), carbon dioxide (not Carbon Dioxide ), and so on, unless you are introducing an acronym (see Mechanics: Using abbreviations ).

Using hyphens

Punctuating text.

Punctuation has many rules in English; here are three that are often a challenge for non-native speakers.

As a rule, insert a comma between the subject of the main clause and whatever comes in front of it, no matter how short, as in "Surprisingly, the temperature did not increase." This comma is not always required, but it often helps and never hurts the meaning of a sentence, so it is good practice.

In series of three or more items, separate items with commas ( red, white, and blue ; yesterday, today, or tomorrow ). Do not use a comma for a series of two items ( black and white ).

In displayed lists, use the same punctuation as you would in normal text (but consider dropping the and ).

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The system is fast, flexible, reliable.

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Scientific Writing

Thinking and writing like a scientist, writing clearly, citation help.

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Helpful Links

For more information on writing your scientific papers, here are some good resources:

http://writingcenter.unc.edu/handouts/scientific-reports/

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1559667/

The Scientific Method

When writing or reading about science, it is useful to keep the scientific method in mind. The scientific method is used as a model to construct writing that can be shared with others in a logical and informative way. Any piece of scientific writing is informative and persuasive: informative because the author is telling the audience how he or she conducted their research and what new information they learned, and persuasive because science papers demonstrate how that new information was obtained and what conclusions can be drawn from the data collected. The format of most journal articles follows the steps of the scientific method, with Introduction, Methods, and Results sections at a minimum. 

Evidence and Argumentation

Science writing has a persuasive element to it. Researchers need to convince others that they have done their experiments properly and that they have answered their central research questions. Therefore, all science papers, even theoretical ones, make use of evidence to support their points. Remember that statistical measures, while extremely useful, are not the only source of evidence. Observations of even a single event can be useful in the right context. Remember to use logic to link your evidence and claims together!

Relating evidence and ideas:  http://undsci.berkeley.edu/article/coreofscience_01

Clarity and Reader Expectations

Many people complain that scientific literature is difficult to understand because of the complicated language and the use of jargon. However, scientific literature can be difficult to understand even if one is familiar with the concepts being discussed.

To avoid confusing the reader, writers should focus on writing clearly and keeping the reader’s expectations in mind. In a scientific paper, it is important that most readers will agree with the information being presented. Writing in a clear and concise way helps the writer accomplish this. Use the paper as a story-telling medium. Concentrate on showing the reader that your experiments show definite conclusion, and how this contribution changes the state of knowledge in the field.

Gopen and Swan (1990) offer the following seven easy ways to make your writing more clear and to say what you want the reader to hear.

• Follow a grammatical subject as soon as possible with its verb.
• Place in the stress position the "new information" you want the reader to emphasize. 
• Place the person or thing whose "story" a sentence is telling at the beginning of the sentence, in the topic position. 
• Place appropriate "old information" (material already stated in the discourse) in the topic position for linkage backward and contextualization forward. 
• Articulate the action of every clause or sentence in its verb. 
• In general, provide context for your reader before asking that reader to consider anything new. 
• In general, try to ensure that the relative emphases of the substance coincide with the relative expectations for emphasis raised by the structure.

[Gopen, G. and Swan, J. 1990. The Science of Scientific Writing. American Scientist. Available here: https://www.americanscientist.org/blog/the-long-view/the-science-of-scientific-writing  ]

One major problem many students have when they start writing papers is using so called “running jumps.” This is the placement of unnecessary words at the beginning of a sentence. For example:

RUNNING JUMP: According to the researchers, the control group showed more change in chlorophyll production (Smith et. al., 2014).

NO RUNNING JUMP: The control group showed more change in chlorophyll production (Smith et. al., 2014).

We’ve already cited a study, so it’s clear that we are referring to researchers and their findings. So the first part of the sentence is unnecessary. 

Try to limit the number of ideas expressed in a single sentence. If a sentence seems like it is trying to say more than two things at once, split it into two sentences.  If a sentence is long and tangled and just doesn't make sense, don't try to perform "surgery" to fix the sentence. Instead, "kill" the sentence and start over, using short, direct sentences to express what you mean.

If you can make a noun phrase into a verb, do it! For example, made note of = noted, provided a similar opinion = agreed with, conducted an experiment = experimented, etc.

Avoid the Passive Voice Where You Can

If a sentence is written in the passive voice, the subject of the sentence (person/thing doing the action) does not come first; rather, the object of the sentence (person/thing not doing the action) is the first noun in the sentence. 

PASSIVE: Radiation was the mechanism by which the samples were sterilized.

MORE ACTIVE: The samples were sterilized using radiation.

ACTIVE: We sterilized the samples using radiation.

Professors (and scientific journals) have differing opinions on the use of passive voice. Some consider it unacceptable, but many are more lenient. And in fact, often it will make more sense to write in the passive voice in certain sections (i.e. Methods) and when you can't use first-person pronouns like "I." In any case, reducing overuse of passive voice in your writing makes it more concise and easier to understand.

Here's a useful link for clear scientific writing style: http://www.nature.com/scitable/topicpage/effective-writing-13815989

Posters are often used as an accompaniment to a talk or presentation, or as a substitute. You’ve probably seen posters hanging up around campus, showcasing students' research. The idea of a poster is to simplify a study and present it in a visual way, so it can be understood by a wide audience. The most important thing to remember when designing a poster (or completing any kind of published work) is to follow the guidelines given. If your instructor, or the conference you’re presenting at, wants a certain format, adhere to that format. These three rules are especially important to follow:

• Shorter is better: make sure that your poster does not contain too much text! Packing text onto the poster makes it difficult to read and understand.
• Bigger is better. No, this is not a contradiction of rule 1! Make sure your text is large enough to read, and readable against the background of the poster.
• Use images. The key aspect of a poster is that it is a visual medium. Include graphs, photos, and illustrations of your work.

Here are some excellent tips and templates for research posters: 

1.  http://colinpurrington.com/tips/academic/posterdesign

2. https://ph.byu.edu/resources

3.  https://pop.psu.edu/sites/pri/files/Poster%20Design%20Tips.pdf : Poster tips from Penn State

Most scientific citation styles are based on APA format. It’s totally okay to use a resource to look up how to format a paper in APA style! As you become more familiar with the format, you will become less reliant on these resources, but for now, here are some sites that may be useful .

Our APA guide

APA Style Official Website

• Last Updated: Apr 25, 2024 2:50 PM
• URL: https://research.ewu.edu/writers_center_sci_writing

Writing the Scientific Paper

When you write about scientific topics to specialists in a particular scientific field, we call that scientific writing. (When you write to non-specialists about scientific topics, we call that science writing.)

The scientific paper has developed over the past three centuries into a tool to communicate the results of scientific inquiry. The main audience for scientific papers is extremely specialized. The purpose of these papers is twofold: to present information so that it is easy to retrieve, and to present enough information that the reader can duplicate the scientific study. A standard format with six main part helps readers to find expected information and analysis:

• Title--subject and what aspect of the subject was studied.
• Abstract--summary of paper: The main reason for the study, the primary results, the main conclusions
• Introduction-- why the study was undertaken
• Methods and Materials-- how the study was undertaken
• Results-- what was found
• Discussion-- why these results could be significant (what the reasons might be for the patterns found or not found)

There are many ways to approach the writing of a scientific paper, and no one way is right. Many people, however, find that drafting chunks in this order works best: Results, Discussion, Introduction, Materials & Methods, Abstract, and, finally, Title.

The title should be very limited and specific. Really, it should be a pithy summary of the article's main focus.

• "Renal disease susceptibility and hypertension are under independent genetic control in the fawn hooded rat"
• "Territory size in Lincoln's Sparrows ( Melospiza lincolnii )"
• "Replacement of deciduous first premolars and dental eruption in archaeocete whales"
• "The Radio-Frequency Single-Electron Transistor (RF-SET): A Fast and Ultrasensitive Electrometer"

This is a summary of your article. Generally between 50-100 words, it should state the goals, results, and the main conclusions of your study. You should list the parameters of your study (when and where was it conducted, if applicable; your sample size; the specific species, proteins, genes, etc., studied). Think of the process of writing the abstract as taking one or two sentences from each of your sections (an introductory sentence, a sentence stating the specific question addressed, a sentence listing your main techniques or procedures, two or three sentences describing your results, and one sentence describing your main conclusion).

Example One

Hypertension, diabetes and hyperlipidemia are risk factors for life-threatening complications such as end-stage renal disease, coronary artery disease and stroke. Why some patients develop complications is unclear, but only susceptibility genes may be involved. To test this notion, we studied crosses involving the fawn-hooded rat, an animal model of hypertension that develops chronic renal failure. Here, we report the localization of two genes, Rf-1 and Rf-2 , responsible for about half of the genetic variation in key indices of renal impairment. In addition, we localize a gene, Bpfh-1 , responsible for about 26% of the genetic variation in blood pressure. Rf-1 strongly affects the risk of renal impairment, but has no significant effect on blood pressure. Our results show that susceptibility to a complication of hypertension is under at least partially independent genetic control from susceptibility to hypertension itself.

Brown, Donna M, A.P. Provoost, M.J. Daly, E.S. Lander, & H.J. Jacob. 1996. "Renal disease susceptibility and hypertension are under indpendent genetic control in the faun-hooded rat." Nature Genetics , 12(1):44-51.

Example Two

We studied survival of 220 calves of radiocollared moose ( Alces alces ) from parturition to the end of July in southcentral Alaska from 1994 to 1997. Prior studies established that predation by brown bears ( Ursus arctos ) was the primary cause of mortality of moose calves in the region. Our objectives were to characterize vulnerability of moose calves to predation as influenced by age, date, snow depths, and previous reproductive success of the mother. We also tested the hypothesis that survival of twin moose calves was independent and identical to that of single calves. Survival of moose calves from parturition through July was 0.27 ± 0.03 SE, and their daily rate of mortality declined at a near constant rate with age in that period. Mean annual survival was 0.22 ± 0.03 SE. Previous winter's snow depths or survival of the mother's previous calf was not related to neonatal survival. Selection for early parturition was evidenced in the 4 years of study by a 6.3% increase in the hazard of death with each daily increase in parturition date. Although there was no significant difference in survival of twin and single moose calves, most twins that died disappeared together during the first 15 days after birth and independently thereafter, suggesting that predators usually killed both when encountered up to that age.

Key words: Alaska, Alces alces , calf survival, moose, Nelchina, parturition synchrony, predation

Testa, J.W., E.F. Becker, & G.R. Lee. 2000. "Temporal patterns in the survival of twin and single moose ( alces alces ) calves in southcentral Alaska." Journal of Mammalogy , 81(1):162-168.

Example Three

We monitored breeding phenology and population levels of Rana yavapaiensis by use of repeated egg mass censuses and visual encounter surveys at Agua Caliente Canyon near Tucson, Arizona, from 1994 to 1996. Adult counts fluctuated erratically within each year of the study but annual means remained similar. Juvenile counts peaked during the fall recruitment season and fell to near zero by early spring. Rana yavapaiensis deposited eggs in two distinct annual episodes, one in spring (March-May) and a much smaller one in fall (September-October). Larvae from the spring deposition period completed metamorphosis in earlv summer. Over the two years of study, 96.6% of egg masses successfully produced larvae. Egg masses were deposited during periods of predictable, moderate stream flow, but not during seasonal periods when flash flooding or drought were likely to affect eggs or larvae. Breeding phenology of Rana yavapaiensis is particularly well suited for life in desert streams with natural flow regimes which include frequent flash flooding and drought at predictable times. The exotic predators of R. yavapaiensis are less able to cope with fluctuating conditions. Unaltered stream flow regimes that allow natural fluctuations in stream discharge may provide refugia for this declining ranid frog from exotic predators by excluding those exotic species that are unable to cope with brief flash flooding and habitat drying.

Sartorius, Shawn S., and Philip C. Rosen. 2000. "Breeding phenology of the lowland leopard frog ( Rana yavepaiensis )." Southwestern Naturalist , 45(3): 267-273.

Introduction

The introduction is where you sketch out the background of your study, including why you have investigated the question that you have and how it relates to earlier research that has been done in the field. It may help to think of an introduction as a telescoping focus, where you begin with the broader context and gradually narrow to the specific problem addressed by the report. A typical (and very useful) construction of an introduction proceeds as follows:

"Echimyid rodents of the genus Proechimys (spiny rats) often are the most abundant and widespread lowland forest rodents throughout much of their range in the Neotropics (Eisenberg 1989). Recent studies suggested that these rodents play an important role in forest dynamics through their activities as seed predators and dispersers of seeds (Adler and Kestrell 1998; Asquith et al 1997; Forget 1991; Hoch and Adler 1997)." (Lambert and Adler, p. 70)

"Our laboratory has been involved in the analysis of the HLA class II genes and their association with autoimmune disorders such as insulin-dependent diabetes mellitus. As part of this work, the laboratory handles a large number of blood samples. In an effort to minimize the expense and urgency of transportation of frozen or liquid blood samples, we have designed a protocol that will preserve the integrity of lymphocyte DNA and enable the transport and storage of samples at ambient temperatures." (Torrance, MacLeod & Hache, p. 64)

"Despite the ubiquity and abundance of P. semispinosus , only two previous studies have assessed habitat use, with both showing a generalized habitat use. [brief summary of these studies]." (Lambert and Adler, p. 70)

"Although very good results have been obtained using polymerase chain reaction (PCR) amplification of DNA extracted from dried blood spots on filter paper (1,4,5,8,9), this preservation method yields limited amounts of DNA and is susceptible to contamination." (Torrance, MacLeod & Hache, p. 64)

"No attempt has been made to quantitatively describe microhabitat characteristics with which this species may be associated. Thus, specific structural features of secondary forests that may promote abundance of spiny rats remains unknown. Such information is essential to understand the role of spiny rats in Neotropical forests, particularly with regard to forest regeneration via interactions with seeds." (Lambert and Adler, p. 71)

"As an alternative, we have been investigating the use of lyophilization ("freeze-drying") of whole blood as a method to preserve sufficient amounts of genomic DNA to perform PCR and Southern Blot analysis." (Torrance, MacLeod & Hache, p. 64)

"We present an analysis of microhabitat use by P. semispinosus in tropical moist forests in central Panama." (Lambert and Adler, p. 71)

"In this report, we summarize our analysis of genomic DNA extracted from lyophilized whole blood." (Torrance, MacLeod & Hache, p. 64)

Methods and Materials

In this section you describe how you performed your study. You need to provide enough information here for the reader to duplicate your experiment. However, be reasonable about who the reader is. Assume that he or she is someone familiar with the basic practices of your field.

It's helpful to both writer and reader to organize this section chronologically: that is, describe each procedure in the order it was performed. For example, DNA-extraction, purification, amplification, assay, detection. Or, study area, study population, sampling technique, variables studied, analysis method.

Include in this section:

• study design: procedures should be listed and described, or the reader should be referred to papers that have already described the used procedure
• particular techniques used and why, if relevant
• modifications of any techniques; be sure to describe the modification
• specialized equipment, including brand-names
• temporal, spatial, and historical description of study area and studied population
• assumptions underlying the study
• statistical methods, including software programs

Example description of activity

Chromosomal DNA was denatured for the first cycle by incubating the slides in 70% deionized formamide; 2x standard saline citrate (SSC) at 70ºC for 2 min, followed by 70% ethanol at -20ºC and then 90% and 100% ethanol at room temperature, followed by air drying. (Rouwendal et al ., p. 79)

Example description of assumptions

We considered seeds left in the petri dish to be unharvested and those scattered singly on the surface of a tile to be scattered and also unharvested. We considered seeds in cheek pouches to be harvested but not cached, those stored in the nestbox to be larderhoarded, and those buried in caching sites within the arena to be scatterhoarded. (Krupa and Geluso, p. 99)

Examples of use of specialized equipment

• Oligonucleotide primers were prepared using the Applied Biosystems Model 318A (Foster City, CA) DNA Synthesizer according to the manufacturers' instructions. (Rouwendal et al ., p.78)
• We first visually reviewed the complete song sample of an individual using spectrograms produced on a Princeton Applied Research Real Time Spectrum Analyzer (model 4512). (Peters et al ., p. 937)

Example of use of a certain technique

Frogs were monitored using visual encounter transects (Crump and Scott, 1994). (Sartorius and Rosen, p. 269)

Example description of statistical analysis

We used Wilcox rank-sum tests for all comparisons of pre-experimental scores and for all comparisons of hue, saturation, and brightness scores between various groups of birds ... All P -values are two-tailed unless otherwise noted. (Brawner et al ., p. 955)

This section presents the facts--what was found in the course of this investigation. Detailed data--measurements, counts, percentages, patterns--usually appear in tables, figures, and graphs, and the text of the section draws attention to the key data and relationships among data. Three rules of thumb will help you with this section:

• present results clearly and logically
• avoid excess verbiage
• consider providing a one-sentence summary at the beginning of each paragraph if you think it will help your reader understand your data

Remember to use table and figures effectively. But don't expect these to stand alone.

Some examples of well-organized and easy-to-follow results:

• Size of the aquatic habitat at Agua Caliente Canyon varied dramatically throughout the year. The site contained three rockbound tinajas (bedrock pools) that did not dry during this study. During periods of high stream discharge seven more seasonal pools and intermittent stretches of riffle became available. Perennial and seasonal pool levels remained stable from late February through early May. Between mid-May and mid-July seasonal pools dried until they disappeared. Perennial pools shrank in surface area from a range of 30-60 m² to 3-5- M². (Sartorius and Rosen, Sept. 2000: 269)

Notice how the second sample points out what is important in the accompanying figure. It makes us aware of relationships that we may not have noticed quickly otherwise and that will be important to the discussion.

A similar test result is obtained with a primer derived from the human ß-satellite... This primer (AGTGCAGAGATATGTCACAATG-CCCC: Oligo 435) labels 6 sites in the PRINS reaction: the chromosomes 1, one pair of acrocentrics and, more weakly, the chromosomes 9 (Fig. 2a). After 10 cycles of PCR-IS, the number of sites labeled has doubled (Fig. 2b); after 20 cycles, the number of sites labeled is the same but the signals are stronger (Fig. 2c) (Rouwendal et al ., July 93:80).

Related Information: Use Tables and Figures Effectively

Do not repeat all of the information in the text that appears in a table, but do summarize it. For example, if you present a table of temperature measurements taken at various times, describe the general pattern of temperature change and refer to the table.

"The temperature of the solution increased rapidly at first, going from 50º to 80º in the first three minutes (Table 1)."

You don't want to list every single measurement in the text ("After one minute, the temperature had risen to 55º. After two minutes, it had risen to 58º," etc.). There is no hard and fast rule about when to report all measurements in the text and when to put the measurements in a table and refer to them, but use your common sense. Remember that readers have all that data in the accompanying tables and figures, so your task in this section is to highlight key data, changes, or relationships.

In this section you discuss your results. What aspect you choose to focus on depends on your results and on the main questions addressed by them. For example, if you were testing a new technique, you will want to discuss how useful this technique is: how well did it work, what are the benefits and drawbacks, etc. If you are presenting data that appear to refute or support earlier research, you will want to analyze both your own data and the earlier data--what conditions are different? how much difference is due to a change in the study design, and how much to a new property in the study subject? You may discuss the implication of your research--particularly if it has a direct bearing on a practical issue, such as conservation or public health.

This section centers on speculation . However, this does not free you to present wild and haphazard guesses. Focus your discussion around a particular question or hypothesis. Use subheadings to organize your thoughts, if necessary.

This section depends on a logical organization so readers can see the connection between your study question and your results. One typical approach is to make a list of all the ideas that you will discuss and to work out the logical relationships between them--what idea is most important? or, what point is most clearly made by your data? what ideas are subordinate to the main idea? what are the connections between ideas?

Achieving the Scientific Voice

Eight tips will help you match your style for most scientific publications.

• Develop a precise vocabulary: read the literature to become fluent, or at least familiar with, the sort of language that is standard to describe what you're trying to describe.
• Once you've labeled an activity, a condition, or a period of time, use that label consistently throughout the paper. Consistency is more important than creativity.
• Define your terms and your assumptions.
• Include all the information the reader needs to interpret your data.
• Remember, the key to all scientific discourse is that it be reproducible . Have you presented enough information clearly enough that the reader could reproduce your experiment, your research, or your investigation?
• When describing an activity, break it down into elements that can be described and labeled, and then present them in the order they occurred.
• When you use numbers, use them effectively. Don't present them so that they cause more work for the reader.
• Include details before conclusions, but only include those details you have been able to observe by the methods you have described. Do not include your feelings, attitudes, impressions, or opinions.
• Research your format and citations: do these match what have been used in current relevant journals?
• Run a spellcheck and proofread carefully. Read your paper out loud, and/ or have a friend look over it for misspelled words, missing words, etc.

Applying the Principles, Example 1

The following example needs more precise information. Look at the original and revised paragraphs to see how revising with these guidelines in mind can make the text clearer and more informative:

Before: Each male sang a definite number of songs while singing. They start with a whistle and then go from there. Each new song is always different, but made up an overall repertoire that was completed before starting over again. In 16 cases (84%), no new songs were sung after the first 20, even though we counted about 44 songs for each bird.

After: Each male used a discrete number of song types in his singing. Each song began with an introductory whistle, followed by a distinctive, complex series of fluty warbles (Fig. 1). Successive songs were always different, and five of the 19 males presented their entire song repertoire before repeating any of their song types (i.e., the first IO recorded songs revealed the entire repertoire of 10 song types). Each song type recurred in long sequences of singing, so that we could be confident that we had recorded the entire repertoire of commonly used songs by each male. For 16 of the 19 males, no new song types were encountered after the first 20 songs, even though we analyzed and average of 44 songs/male (range 30-59).

Applying the Principles, Example 2

In this set of examples, even a few changes in wording result in a more precise second version. Look at the original and revised paragraphs to see how revising with these guidelines in mind can make the text clearer and more informative:

Before: The study area was on Mt. Cain and Maquilla Peak in British Columbia, Canada. The study area is about 12,000 ha of coastal montane forest. The area is both managed and unmanaged and ranges from 600-1650m. The most common trees present are mountain hemlock ( Tsuga mertensiana ), western hemlock ( Tsuga heterophylla ), yellow cedar ( Chamaecyparis nootkatensis ), and amabilis fir ( Abies amabilis ).

After: The study took place on Mt. Cain and Maquilla Peak (50'1 3'N, 126'1 8'W), Vancouver Island, British Columbia. The study area encompassed 11,800 ha of coastal montane forest. The landscape consisted of managed and unmanaged stands of coastal montane forest, 600-1650 m in elevation. The dominant tree species included mountain hemlock ( Tsuga mertensiana ), western hemlock ( Tsuga heterophylla ), yellow cedar ( Chamaecyparis nootkatensis ), and amabilis fir ( Abies amabilis ).

Two Tips for Sentence Clarity

Although you will want to consider more detailed stylistic revisions as you become more comfortable with scientific writing, two tips can get you started:

First, the verb should follow the subject as soon as possible.

Really Hard to Read : "The smallest of the URF's (URFA6L), a 207-nucleotide (nt) reading frame overlapping out of phase the NH2- terminal portion of the adenosinetriphosphatase (ATPase) subunit 6 gene has been identified as the animal equivalent of the recently discovered yeast H+-ATPase subunit gene."

Less Hard to Read : "The smallest of the UR-F's is URFA6L, a 207-nucleotide (nt) reading frame overlapping out of phase the NH2-terminal portion of the adenosinetriphosphatase (ATPase) subunit 6 gene; it has been identified as the animal equivalent of the recently discovered yeast H+-ATPase subunit 8 gene."

Second, place familiar information first in a clause, a sentence, or a paragraph, and put the new and unfamiliar information later.

More confusing : The epidermis, the dermis, and the subcutaneous layer are the three layers of the skin. A layer of dead skin cells makes up the epidermis, which forms the body's shield against the world. Blood vessels, carrying nourishment, and nerve endings, which relay information about the outside world, are found in the dermis. Sweat glands and fat cells make up the third layer, the subcutaneous layer.

Less confusing : The skin consists of three layers: the epidermis, the dermis, and the subcutaneous layer. The epidermis is made up of dead skin cells, and forms a protective shield between the body and the world. The dermis contains the blood vessels and nerve endings that nourish the skin and make it receptive to outside stimuli. The subcutaneous layer contains the sweat glands and fat cells which perform other functions of the skin.

Bibliography

• Scientific Writing for Graduate Students . F. P. Woodford. Bethesda, MD: Council of Biology Editors, 1968. [A manual on the teaching of writing to graduate students--very clear and direct.]
• Scientific Style and Format . Council of Biology Editors. Cambridge: Cambridge University Press, 1994.
• "The science of scientific writing." George Gopen and Judith Swann. The American Scientist , Vol. 78, Nov.-Dec. 1990. Pp 550-558.
• "What's right about scientific writing." Alan Gross and Joseph Harmon. The Scientist , Dec. 6 1999. Pp. 20-21.
• "A Quick Fix for Figure Legends and Table Headings." Donald Kroodsma. The Auk , 117 (4): 1081-1083, 2000.

Wortman-Wunder, Emily, & Kate Kiefer. (1998). Writing the Scientific Paper. Writing@CSU . Colorado State University. https://writing.colostate.edu/resources/writing/guides/.

Definition and Examples of Science Writing

Glossary of Grammatical and Rhetorical Terms

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• An Introduction to Punctuation
• Ph.D., Rhetoric and English, University of Georgia
• M.A., Modern English and American Literature, University of Leicester
• B.A., English, State University of New York

The term science writing refers to  writing about a scientific subject matter, often in a non-technical manner for an audience of non-scientists (a form of journalism or creative nonfiction ). Also called popular science writing . (Definition No. 1)

Science writing may also refer to writing that reports scientific observations and results in a manner governed by specific conventions (a form of technical writing ). More commonly known as scientific writing . (Definition No. 2)

Examples and Observations

• "Because science writing is intended to be entertaining enough to capture the continued interest of potential readers, its style is much less somber than the usual scientific writing [i.e., definition No. 2, above]. The use of slang , puns , and other word plays on the English language  are accepted and even encouraged. . . . "Distinguishing between science writing and scientific writing is reasonable—they have different purposes and a different audience . However, one would be ill-advised to use the term 'science writing' or 'popular writing' in a disparaging way. Writing (or providing consultation for others who are writing) popularized accounts based on scientific research should be an important part of every scientists' outreach activities. The wider community is essential to adequate support for scientific endeavors."
• An Example of Science Writing: "Stripped for Parts":  "Sustaining a dead body until its organs can be harvested is a tricky process requiring the latest in medical technology. But it's also a distinct anachronism in an era when medicine is becoming less and less invasive. Fixing blocked coronary arteries, which not long ago required prying a patient's chest open with a saw and spreader, can now be accomplished with a tiny stent delivered to the heart on a slender wire threaded up the leg. Exploratory surgery has given way to robot cameras and high-resolution imaging. Already, we are eyeing the tantalizing summit of gene therapy, where diseases are cured even before they do damage. Compared with such microscale cures, transplants—which consist of salvaging entire organs from a heart-beating cadaver and sewing them into a different body—seem crudely mechanical, even medieval."

On Explaining Science

"The question is not "should" you explain a concept or process, but "how" can you do so in a way that is clear and so readable that it is simply part of the story?

"Use explanatory strategies such as ...

- "People who study what makes an explanation successful have found that while giving examples is helpful, giving nonexamples is even better. "Nonexamples are examples of what something is not . Often, that kind of example will help clarify what the thing is . If you were trying to explain groundwater, for instance, you might say that, while the term seems to suggest an actual body of water, such as a lake or an underground river, that would be an inaccurate image. Groundwater is not a body of water in the traditional sense; rather, as Katherine Rowan, communications professor, points out, it is water moving slowly but relentlessly through cracks and crevices in the ground below us... "Be acutely aware of your readers' beliefs. You might write that chance is the best explanation of a disease cluster; but this could be counterproductive if your readers reject chance as an explanation for anything. If you are aware that readers' beliefs may collide with an explanation you give, you may be able to write in a way that doesn't cause these readers to block their minds to the science you explain."

The Lighter Side of Science Writing

"In this paragraph I will state the main claim that the research makes, making appropriate use of ' scare quotes ' to ensure that it's clear that I have no opinion about this research whatsoever.

"In this paragraph, I will briefly (because no paragraph should be more than one line) state which existing scientific ideas this new research 'challenges.'

"If the research is about a potential cure or a solution to a problem, this paragraph will describe how it will raise hopes for a group of sufferers or victims.

"This paragraph elaborates on the claim, adding weasel-words like 'the scientists say' to shift responsibility for establishing the likely truth or accuracy of the research findings on to absolutely anybody else but me, the journalist. ..."

(Janice R. Matthews and Robert W. Matthews,  Successful Scientific Writing: A Step-by-Step Guide for the Biological and Medical Sciences , 4th ed. Cambridge University Press, 2014)

(Jennifer Kahn, "Stripped for Parts." Wired.   March 2003. Reprinted in The Best American Science Writing 2004 , edited by Dava Sobel. HarperCollins, 2004)

(Sharon Dunwoody, "On Explaining Science." A Field Guide for Science Writers , 2nd ed., ed. by Deborah Blum, Mary Knudson, and Robin Marantz Henig. Oxford University Press, 2006)

(Martin Robbins, "This Is a News Website Article About a Scientific Paper." The Guardian , September 27, 2010)

• Definition and Examples of Explication (Analysis)
• Theory Definition in Science
• Definition and Examples of Paragraphing in Essays
• Understanding Organization in Composition and Speech
• Definition and Examples of a Transition in Composition
• Definition and Examples of Body Paragraphs in Composition
• Premise Definition and Examples in Arguments
• Definition and Examples of Paragraph Breaks in Prose
• Genres in Literature
• Supporting Detail in Composition and Speech
• Topic In Composition and Speech
• Conciseness for Better Composition
• Development in Composition: Building an Essay
• active verb (action verb)
• Paragraph Transition: Definition and Examples
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Tools & Methods

How to successfully write a scientific essay.

Posted by Cody Rhodes

If you are undertaking a course which relates to science, you are more or less apt to write an essay on science. You need to know how to write a science essay irrespective of whether your professor gives you a topic or you come up with one. Additionally, you need to have an end objective in mind. Writing a science essay necessitates that you produce an article which has all the details and facts about the subject matter and it ought to be to the point. Also, you need to know and understand that science essays are more or less different from other types of essays. They require you to be analytical and precise when answering questions. Hence, this can be quite challenging and tiresome. However, that should not deter you from learning how to write your paper. You can always inquire for pre-written research papers for sale from writing services like EssayZoo.

Also, you can read other people’s articles and find out how they produce and develop unique and high-quality papers. Moreover, this will help you understand how to approach your essays in different ways. Nonetheless, if you want to learn how to write a scientific paper in a successful manner, consider the following tips.

Select a topic for your article Like any other type of essay, you need to have a topic before you start the actual writing process. Your professor or instructor may give you a science essay topic to write about or ask you to come up with yours. When selecting a topic for your paper, ensure that you choose one you can write about. Do not pick a complex topic which can make the writing process boring and infuriating for you. Instead, choose one that you are familiar with. Select a topic you will not struggle gathering information about. Also, you need to have an interest in it. If you are unable to come up with a good topic, trying reading other people’s articles. This will help you develop a topic with ease.

Draft a plan After selecting a topic, the next step is drafting a plan or an outline. An outline is fundamental in writing a scientific essay as it is the foundation on which your paper is built. Additionally, it acts as a road map for your article. Hence, you need to incorporate all the thoughts and ideas you will include in your essay in the outline. You need to know what you will include in the introduction, the body, and the conclusion. Drafting a plan helps you save a lot of time when writing your paper. Also, it helps you to keep track of the primary objective of your article.

Start writing the article After drafting a plan, you can begin the writing process. Writing your paper will be smooth and easier as you have an outline which helps simplify the writing process. When writing your article, begin with a strong hook for your introduction. Dictate the direction your paper will take. Provide some background information and state the issue you will discuss as well as the solutions you have come up with. Arrange the body of your article according to the essay structure you will use to guide you. Also, ensure that you use transitory sentences to show the relationship between the paragraphs of your article. Conclude your essay by summarizing all the key points. Also, highlight the practical potential of our findings and their impacts.

Proofread and check for errors in the paper Before submitting or forwarding your article, it is fundamental that you proofread and correct all the errors that you come across. Delivering a paper that is full of mistakes can affect your overall performance in a negative manner. Thus, it is essential you revise your paper and check for errors. Correct all of them. Ask a friend to proofread your paper. He or she may spot some of the mistakes you did not come across.

In conclusion, writing a scientific essay differs from writing other types of papers. A scientific essay requires you to produce an article which has all the information and facts about the subject matter and it ought to be to the point. Nonetheless, the scientific essay formats similar to the format of any other essay: introduction, body, and conclusion. You need to use your outline to guide you through the writing process. To learn how to write a scientific essay in a successful manner, consider the tips above.

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Understanding Science

How science REALLY works...

• Understanding Science 101
• Misconceptions

Misconception: Science is a collection of facts.

Correction: Science is both a body of knowledge and the process for building that knowledge. Read more about it.

What is science?

The word “ science ” probably brings to mind many different pictures: a fat textbook, white lab coats and microscopes, an astronomer peering through a telescope, a naturalist in the rainforest, Einstein’s equations scribbled on a chalkboard, the launch of the space shuttle, bubbling beakers …. All of those images reflect some aspect of science. But none of them provides a full picture because science has so many facets:

• Science is both a body of knowledge and a process. In school, science may sometimes seem like a collection of isolated and static  facts listed in a textbook, but that’s only a small part of the story. Just as importantly, science is also a process of discovery that allows us to link isolated facts into coherent and comprehensive understandings of the natural world .
• Science is exciting. Science is a way of discovering what’s in the universe and how those things work today, how they worked in the past, and how they are likely to work in the future. Scientists are motivated by the thrill of seeing or figuring out something that no one has before.
• Science is useful. The knowledge generated by science is powerful and reliable. It can be used to develop new technologies , treat diseases, and deal with many other sorts of problems.
• Science is ongoing. Science is continually refining and expanding our knowledge of the universe, and as it does, it leads to new questions for future investigation. Science will never be “finished.”
• Science is a global human endeavor. People all over the world participate in the process of science. And you can too!

This section describes what makes science science. You can investigate:

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Scientific Objectivity

Scientific objectivity is a property of various aspects of science. It expresses the idea that scientific claims, methods, results—and scientists themselves—are not, or should not be, influenced by particular perspectives, value judgments, community bias or personal interests, to name a few relevant factors. Objectivity is often considered to be an ideal for scientific inquiry, a good reason for valuing scientific knowledge, and the basis of the authority of science in society.

Many central debates in the philosophy of science have, in one way or another, to do with objectivity: confirmation and the problem of induction; theory choice and scientific change; realism; scientific explanation; experimentation; measurement and quantification; statistical evidence; reproducibility; evidence-based science; feminism and values in science. Understanding the role of objectivity in science is therefore integral to a full appreciation of these debates. As this article testifies, the reverse is true too: it is impossible to fully appreciate the notion of scientific objectivity without touching upon many of these debates.

The ideal of objectivity has been criticized repeatedly in philosophy of science, questioning both its desirability and its attainability. This article focuses on the question of how scientific objectivity should be defined , whether the ideal of objectivity is desirable , and to what extent scientists can achieve it.

1. Introduction

2.1 the view from nowhere, 2.2 theory-ladenness and incommensurability, 2.3 underdetermination, values, and the experimenters’ regress, 3.1 epistemic and contextual values, 3.2 acceptance of scientific hypotheses and value neutrality, 3.3 science, policy and the value-free ideal, 4.1 measurement and quantification, 4.2.1 bayesian inference, 4.2.2 frequentist inference, 4.3 feyerabend: the tyranny of the rational method, 5.1 reproducibility and the meta-analytic perspective, 5.2 feminist and standpoint epistemology, 6.1 max weber and objectivity in the social sciences, 6.2 contemporary rational choice theory, 6.3 evidence-based medicine and social policy, 7. the unity and disunity of scientific objectivity, 8. conclusions, other internet resources, related entries.

Objectivity is a value. To call a thing objective implies that it has a certain importance to us and that we approve of it. Objectivity comes in degrees. Claims, methods, results, and scientists can be more or less objective, and, other things being equal, the more objective, the better. Using the term “objective” to describe something often carries a special rhetorical force with it. The admiration of science among the general public and the authority science enjoys in public life stems to a large extent from the view that science is objective or at least more objective than other modes of inquiry. Understanding scientific objectivity is therefore central to understanding the nature of science and the role it plays in society.

If what is so great about science is its objectivity, then objectivity should be worth defending. The close examinations of scientific practice that philosophers of science have undertaken in the past fifty years have shown, however, that several conceptions of the ideal of objectivity are either questionable or unattainable. The prospects for a science providing a non-perspectival “view from nowhere” or for proceeding in a way uninformed by human goals and values are fairly slim, for example.

This article discusses several proposals to characterize the idea and ideal of objectivity in such a way that it is both strong enough to be valuable, and weak enough to be attainable and workable in practice. We begin with a natural conception of objectivity: faithfulness to facts . We motivate the intuitive appeal of this conception, discuss its relation to scientific method and discuss arguments challenging both its attainability as well as its desirability. We then move on to a second conception of objectivity as absence of normative commitments and value-freedom , and once more we contrast arguments in favor of such a conception with the challenges it faces. A third conception of objectivity which we discuss at length is the idea of absence of personal bias .

Finally there is the idea that objectivity is anchored in scientific communities and their practices . After discussing three case studies from economics, social science and medicine, we address the conceptual unity of scientific objectivity : Do the various conceptions have a common valid core, such as promoting trust in science or minimizing relevant epistemic risks? Or are they rivaling and only loosely related accounts? Finally we present some conjectures about what aspects of objectivity remain defensible and desirable in the light of the difficulties we have encountered.

2. Objectivity as Faithfulness to Facts

The basic idea of this first conception of objectivity is that scientific claims are objective in so far as they faithfully describe facts about the world. The philosophical rationale underlying this conception of objectivity is the view that there are facts “out there” in the world and that it is the task of scientists to discover, analyze, and systematize these facts. “Objective” then becomes a success word: if a claim is objective, it correctly describes some aspect of the world.

In this view, science is objective to the degree that it succeeds at discovering and generalizing facts, abstracting from the perspective of the individual scientist. Although few philosophers have fully endorsed such a conception of scientific objectivity, the idea figures recurrently in the work of prominent twentieth-century philosophers of science such as Carnap, Hempel, Popper, and Reichenbach.

Humans experience the world from a perspective. The contents of an individual’s experiences vary greatly with his perspective, which is affected by his personal situation, and the details of his perceptual apparatus, language and culture. While the experiences vary, there seems to be something that remains constant. The appearance of a tree will change as one approaches it but—according to common sense and most philosophers—the tree itself doesn’t. A room may feel hot or cold for different persons, but its temperature is independent of their experiences. The object in front of me does not disappear just because the lights are turned off.

These examples motivate a distinction between qualities that vary with one’s perspective, and qualities that remain constant through changes of perspective. The latter are the objective qualities. Thomas Nagel explains that we arrive at the idea of objective qualities in three steps (Nagel 1986: 14). The first step is to realize (or postulate) that our perceptions are caused by the actions of things around us, through their effects on our bodies. The second step is to realize (or postulate) that since the same qualities that cause perceptions in us also have effects on other things and can exist without causing any perceptions at all, their true nature must be detachable from their perspectival appearance and need not resemble it. The final step is to form a conception of that “true nature” independently of any perspective. Nagel calls that conception the “view from nowhere”, Bernard Williams the “absolute conception” (Williams 1985 [2011]). It represents the world as it is, unmediated by human minds and other “distortions”.

This absolute conception lies at the basis of scientific realism (for a detailed discussion, see the entry on scientific realism ) and it is attractive in so far as it provides a basis for arbitrating between conflicting viewpoints (e.g., two different observations). Moreover, the absolute conception provides a simple and unified account of the world. Theories of trees will be very hard to come by if they use predicates such as “height as seen by an observer” and a hodgepodge if their predicates track the habits of ordinary language users rather than the properties of the world. To the extent, then, that science aims to provide explanations for natural phenomena, casting them in terms of the absolute conception would help to realize this aim. A scientific account cast in the language of the absolute conception may not only be able to explain why a tree is as tall as it is but also why we see it in one way when viewed from one standpoint and in a different way when viewed from another. As Williams (1985 [2011: 139]) puts it,

[the absolute conception] nonvacuously explain[s] how it itself, and the various perspectival views of the world, are possible.

A third reason to find the view from nowhere attractive is that if the world came in structures as characterized by it and we did have access to it, we could use our knowledge of it to ground predictions (which, to the extent that our theories do track the absolute structures, will be borne out). A fourth and related reason is that attempts to manipulate and control phenomena can similarly be grounded in our knowledge of these structures. To attain any of the four purposes—settling disagreements, explaining the world, predicting phenomena, and manipulation and control—the absolute conception is at best sufficient but not necessary. We can, for instance, settle disagreements by imposing the rule that the person with higher social rank or greater experience is always right. We can explain the world and our image of it by means of theories that do not represent absolute structures and properties, and there is no need to get things (absolutely) right in order to predict successfully. Nevertheless, there is something appealing in the idea that factual disagreements can be settled by the very facts themselves, that explanations and predictions grounded in what’s really there rather than in a distorted image of it.

No matter how desirable, our ability to use scientific claims to represent facts about the world depends on whether these claims can unambiguously be established on the basis of evidence, and of evidence alone. Alas, the relation between evidence and scientific hypothesis is not straightforward. Subsection 2.2 and subsection 2.3 will look at two challenges of the idea that even the best scientific method will yield claims that describe an aperspectival view from nowhere. Section 5.2 will deal with socially motivated criticisms of the view from nowhere.

According to a popular picture, all scientific theories are false and imperfect. Yet, as we add true and eliminate false beliefs, our best scientific theories become more truthlike (e.g., Popper 1963, 1972). If this picture is correct, then scientific knowledge grows by gradually approaching the truth and it will become more objective over time, that is, more faithful to facts. However, scientific theories often change, and sometimes several theories compete for the place of the best scientific account of the world.

It is inherent in the above picture of scientific objectivity that observations can, at least in principle, decide between competing theories. If they did not, the conception of objectivity as faithfulness would be pointless to have as we would not be in a position to verify it. This position has been adopted by Karl R. Popper, Rudolf Carnap and other leading figures in (broadly) empiricist philosophy of science. Many philosophers have argued that the relation between observation and theory is way more complex and that influences can actually run both ways (e.g., Duhem 1906 [1954]; Wittgenstein 1953 [2001]). The most lasting criticism, however, was delivered by Thomas S. Kuhn (1962 [1970]) in his book “The Structure of Scientific Revolutions”.

Kuhn’s analysis is built on the assumption that scientists always view research problems through the lens of a paradigm, defined by set of relevant problems, axioms, methodological presuppositions, techniques, and so forth. Kuhn provided several historical examples in favor of this claim. Scientific progress—and the practice of normal, everyday science—happens within a paradigm that guides the individual scientists’ puzzle-solving work and that sets the community standards.

Can observations undermine such a paradigm, and speak for a different one? Here, Kuhn famously stresses that observations are “theory-laden” (cf. also Hanson 1958): they depend on a body of theoretical assumptions through which they are perceived and conceptualized. This hypothesis has two important aspects.

First, the meaning of observational concepts is influenced by theoretical assumptions and presuppositions. For example, the concepts “mass” and “length” have different meanings in Newtonian and relativistic mechanics; so does the concept “temperature” in thermodynamics and statistical mechanics (cf. Feyerabend 1962). In other words, Kuhn denies that there is a theory-independent observation language. The “faithfulness to reality” of an observation report is always mediated by a theoretical überbau , disabling the role of observation reports as an impartial, merely fact-dependent arbiter between different theories.

Second, not only the observational concepts, but also the perception of a scientist depends on the paradigm she is working in.

Practicing in different worlds, the two groups of scientists [who work in different paradigms, J.R./J.S.] see different things when they look from the same point in the same direction. (Kuhn 1962 [1970: 150])

That is, our own sense data are shaped and structured by a theoretical framework, and may be fundamentally distinct from the sense data of scientists working in another one. Where a Ptolemaic astronomer like Tycho Brahe sees a sun setting behind the horizon, a Copernican astronomer like Johannes Kepler sees the horizon moving up to a stationary sun. If this picture is correct, then it is hard to assess which theory or paradigm is more faithful to the facts, that is, more objective.

The thesis of the theory-ladenness of observation has also been extended to the incommensurability of different paradigms or scientific theories , problematized independently by Thomas S. Kuhn (1962 [1970]) and Paul Feyerabend (1962). Literally, this concept means “having no measure in common”, and it figures prominently in arguments against a linear and standpoint-independent picture of scientific progress. For instance, the Special Theory of Relativity appears to be more faithful to the facts and therefore more objective than Newtonian mechanics because it reduces, for low speeds, to the latter, and it accounts for some additional facts that are not predicted correctly by Newtonian mechanics. This picture is undermined, however, by two central aspects of incommensurability. First, not only do the observational concepts in both theories differ, but the principles for specifying their meaning may be inconsistent with each other (Feyerabend 1975: 269–270). Second, scientific research methods and standards of evaluation change with the theories or paradigms. Not all puzzles that could be tackled in the old paradigm will be solved by the new one—this is the phenomenon of “Kuhn loss”.

A meaningful use of objectivity presupposes, according to Feyerabend, to perceive and to describe the world from a specific perspective, e.g., when we try to verify the referential claims of a scientific theory. Only within a peculiar scientific worldview, the concept of objectivity may be applied meaningfully. That is, scientific method cannot free itself from the particular scientific theory to which it is applied; the door to standpoint-independence is locked. As Feyerabend puts it:

our epistemic activities may have a decisive influence even upon the most solid piece of cosmological furniture—they make gods disappear and replace them by heaps of atoms in empty space. (1978: 70)

Kuhn and Feyerabend’s theses about theory-ladenness of observation, and their implications for the objectivity of scientific inquiry have been much debated afterwards, and have often been misunderstood in a social constructivist sense. Therefore Kuhn later returned to the topic of scientific objectivity, of which he gives his own characterization in terms of the shared cognitive values of a scientific community. We discuss Kuhn’s later view in section 3.1 . For a more thorough coverage, see the entries on theory and observation in science , the incommensurability of scientific theories and Thomas S. Kuhn .

Scientific theories are tested by comparing their implications with the results of observations and experiments. Unfortunately, neither positive results (when the theory’s predictions are borne out in the data) nor negative results (when they are not) allow unambiguous inferences about the theory. A positive result can obtain even though the theory is false, due to some alternative that makes the same predictions. Finding suspect Jones’ fingerprints on the murder weapon is consistent with his innocence because he might have used it as a kitchen knife. A negative result might be due not to the falsehood of the theory under test but due to the failing of one or more auxiliary assumptions needed to derive a prediction from the theory. Testing, let us say, the implications of Newton’s laws for movements in our planetary system against observations requires assumptions about the number of planets, the sun’s and the planets’ masses, the extent to which the earth’s atmosphere refracts light beams, how telescopes affect the results and so on. Any of these may be false, explaining an inconsistency. The locus classicus for these observations is Pierre Duhem’s The Aim and Structure of Physical Theory (Duhem 1906 [1954]). Duhem concluded that there was no “crucial experiment”, an experiment that conclusively decides between two alternative theories, in physics (1906 [1954: 188ff.]), and that physicists had to employ their expert judgment or what Duhem called “good sense” to determine what an experimental result means for the truth or falsehood of a theory (1906 [1954: 216ff.]).

In other words, there is a gap between the evidence and the theory supported by it. It is important to note that the alleged gap is more profound than the gap between the premisses of any inductive argument and its conclusion, say, the gap between “All hitherto observed ravens have been black” and “All ravens are black”. The latter gap could be bridged by an agreed upon rule of inductive reasoning. Alas, all attempts to find an analogous rule for theory choice have failed (e.g., Norton 2003). Various philosophers, historians, and sociologists of science have responded that theory appraisal is “a complex form of value judgment” (McMullin 1982: 701; see also Kuhn 1977; Hesse 1980; Bloor 1982).

In section 3.1 below we will discuss the nature of the value judgments in more detail. For now the important lesson is that if these philosophers, historians, and sociologists are correct, the “faithfulness to facts” ideal is untenable. As the scientific image of the world is a joint product of the facts and scientists’ value judgments, that image cannot be said to be aperspectival. Science does not eschew the human perspective. There are of course ways to escape this conclusion. If, as John Norton (2003; ms.—see Other Internet Resources) has argued, it is material facts that power and justify inductive inferences, and not value judgments, we can avoid the negative conclusion regarding the view from nowhere. Unsurprisingly, Norton is also critical of the idea that evidence generally underdetermines theory (Norton 2008). However, there are good reasons to mistrust Norton’s optimism regarding the ineliminability of values and other non-factual elements in inductive inferences (Reiss 2020).

There is another, closely related concern. Most of the earlier critics of “objective” verification or falsification focused on the relation between evidence and scientific theories. There is a sense in which the claim that this relation is problematic is not so surprising. Scientific theories contain highly abstract claims that describe states of affairs far removed from the immediacy of sense experience. This is for a good reason: sense experience is necessarily perspectival, so to the extent to which scientific theories are to track the absolute conception, they must describe a world different from that of sense experience. But surely, one might think, the evidence itself is objective. So even if we do have reasons to doubt that abstract theories faithfully represent the world, we should stand on firmer grounds when it comes to the evidence against which we test abstract theories.

Theories are seldom tested against brute observations, however. Simple generalizations such as “all swans are white” are directly learned from observations (say, of the color of swans) but they do not represent the view from nowhere (for one thing, the view from nowhere doesn’t have colors). Genuine scientific theories are tested against experimental facts or phenomena, which are themselves unobservable to the unaided senses. Experimental facts or phenomena are instead established using intricate procedures of measurement and experimentation.

We therefore need to ask whether the results of scientific measurements and experiments can be aperspectival. In an important debate in the 1980s and 1990s some commentators answered that question with a resounding “no”, which was then rebutted by others. The debate concerns the so-called “experimenter’s regress” (Collins 1985). Collins, a prominent sociologist of science, claims that in order to know whether an experimental result is correct, one first needs to know whether the apparatus producing the result is reliable. But one doesn’t know whether the apparatus is reliable unless one knows that it produces correct results in the first place and so on and so on ad infinitum . Collins’ main case concerns attempts to detect gravitational waves, which were very controversially discussed among physicists in the 1970s.

Collins argues that the circle is eventually broken not by the “facts” themselves but rather by factors having to do with the scientist’s career, the social and cognitive interests of his community, and the expected fruitfulness for future work. It is important to note that in Collins’s view these factors do not necessarily make scientific results arbitrary. But what he does argue is that the experimental results do not represent the world according to the absolute conception. Rather, they are produced jointly by the world, scientific apparatuses, and the psychological and sociological factors mentioned above. The facts and phenomena of science are therefore necessarily perspectival.

In a series of contributions, Allan Franklin, a physicist-turned-philosopher of science, has tried to show that while there are indeed no algorithmic procedures for establishing experimental facts, disagreements can nevertheless be settled by reasoned judgment on the basis of bona fide epistemological criteria such as experimental checks and calibration, elimination of possible sources of error, using apparatuses based on well-corroborated theory and so on (Franklin 1994, 1997). Collins responds that “reasonableness” is a social category that is not drawn from physics (Collins 1994).

The main issue for us in this debate is whether there are any reasons to believe that experimental results provide an aperspectival view on the world. According to Collins, experimental results are co-determined by the facts as well as social and psychological factors. According to Franklin, whatever else influences experimental results other than facts is not arbitrary but instead based on reasoned judgment. What he has not shown is that reasoned judgment guarantees that experimental results reflect the facts alone and are therefore aperspectival in any interesting sense. Another important challenge for the aperspectival account comes from feminist epistemology and other accounts that stress the importance of the construction of scientific knowledge through epistemic communities. These accounts are reviewed in section 5 .

3. Objectivity as Absence of Normative Commitments and the Value-Free Ideal

In the previous section we have presented arguments against the view of objectivity as faithfulness to facts and an impersonal “view from nowhere”. An alternative view is that science is objective to the extent that it is value-free . Why would we identify objectivity with value-freedom or regard the latter as a prerequisite for the former? Part of the answer is empiricism. If science is in the business of producing empirical knowledge, and if differences about value judgments cannot be settled by empirical means, values should have no place in science. In the following we will try to make this intuition more precise.

Before addressing what we will call the “value-free ideal”, it will be helpful to distinguish four stages at which values may affect science. They are: (i) the choice of a scientific research problem; (ii) the gathering of evidence in relation to the problem; (iii) the acceptance of a scientific hypothesis or theory as an adequate answer to the problem on the basis of the evidence; (iv) the proliferation and application of scientific research results (Weber 1917 [1949]).

Most philosophers of science would agree that the role of values in science is contentious only with respect to dimensions (ii) and (iii): the gathering of evidence and the acceptance of scientific theories . It is almost universally accepted that the choice of a research problem is often influenced by interests of individual scientists, funding parties, and society as a whole. This influence may make science more shallow and slow down its long-run progress, but it has benefits, too: scientists will focus on providing solutions to those intellectual problems that are considered urgent by society and they may actually improve people’s lives. Similarly, the proliferation and application of scientific research results is evidently affected by the personal values of journal editors and end users, and little can be done about this. The real debate is about whether or not the “core” of scientific reasoning—the gathering of evidence and the assessment and acceptance scientific theories—is, and should be, value-free.

We have introduced the problem of the underdetermination of theory by evidence above. The problem does not stop, however, at values being required for filling the gap between theory and evidence. A further complication is that these values can conflict with each other. Consider the classical problem of fitting a mathematical function to a data set. The researcher often has the choice between using a complex function, which makes the relationship between the variables less simple but fits the data more accurately , or postulating a simpler relationship that is less accurate . Simplicity and accuracy are both important cognitive values, and trading them off requires a careful value judgment. However, philosophers of science tend to regard value-ladenness in this sense as benign. Cognitive values (sometimes also called “epistemic” or “constitutive” values) such as predictive accuracy, scope, unification, explanatory power, simplicity and coherence with other accepted theories are taken to be indicative of the truth of a theory and therefore provide reasons for preferring one theory over another (McMullin 1982, 2009; Laudan 1984; Steel 2010). Kuhn (1977) even claims that cognitive values define the shared commitments of science, that is, the standards of theory assessment that characterize the scientific approach as a whole. Note that not every philosopher entertains the same list of cognitive values: subjective differences in ranking and applying cognitive values do not vanish, a point Kuhn made emphatically.

In most views, the objectivity and authority of science is not threatened by cognitive values, but only by non-cognitive or contextual values . Contextual values are moral, personal, social, political and cultural values such as pleasure, justice and equality, conservation of the natural environment and diversity. The most notorious cases of improper uses of such values involve travesties of scientific reasoning, where the intrusion of contextual values led to an intolerant and oppressive scientific agenda with devastating epistemic and social consequences. In the Third Reich, a large part of contemporary physics, such as the theory of relativity, was condemned because its inventors were Jewish; in the Soviet Union, biologist Nikolai Vavilov was sentenced to death (and died in prison) because his theories of genetic inheritance did not match Marxist-Leninist ideology. Both states tried to foster a science that was motivated by political convictions (“Deutsche Physik” in Nazi Germany, Lysenko’s Lamarckian theory of inheritance and denial of genetics), leading to disastrous epistemic and institutional effects.

Less spectacular, but arguably more frequent are cases where research is biased toward the interests of the sponsors, such as tobacco companies, food manufacturers and large pharmaceutic firms (e.g., Resnik 2007; Reiss 2010). This preference bias , defined by Wilholt (2009) as the infringement of conventional standards of the research community with the aim of arriving at a particular result, is clearly epistemically harmful. Especially for sensitive high-stakes issues such as the admission of medical drugs or the consequences of anthropogenic global warming, it seems desirable that research scientists assess theories without being influenced by such considerations. This is the core idea of the

Value-Free Ideal (VFI): Scientists should strive to minimize the influence of contextual values on scientific reasoning, e.g., in gathering evidence and assessing/accepting scientific theories.

According to the VFI, scientific objectivity is characterized by absence of contextual values and by exclusive commitment to cognitive values in stages (ii) and (iii) of the scientific process. See Dorato (2004: 53–54), Ruphy (2006: 190) or Biddle (2013: 125) for alternative formulations.

For value-freedom to be a reasonable ideal, it must not be a goal beyond reach and be attainable at least to some degree. This claim is expressed by the

Value-Neutrality Thesis (VNT): Scientists can—at least in principle—gather evidence and assess/accept theories without making contextual value judgments.

Unlike the VFI, the VNT is not normative: its subject is whether the judgments that scientists make are, or could possibly be, free of contextual values. Similarly, Hugh Lacey (1999) distinguishes three principal components or aspects of value-free science: impartiality, neutrality and autonomy. Impartiality means that theories are solely accepted or appraised in virtue of their contribution to the cognitive values of science, such as truth, accuracy or explanatory power. This excludes the influence of contextual values, as stated above. Neutrality means that scientific theories make no value statements about the world: they are concerned with what there is, not with what there should be. Finally, scientific autonomy means that the scientific agenda is shaped by the desire to increase scientific knowledge, and that contextual values have no place in scientific method.

These three interpretations of value-free science can be combined with each other, or used individually. All of them, however, are subject to criticisms that we examine below. Denying the VNT, or the attainability of Lacey’s three criteria for value-free science, poses a challenge for scientific objectivity: one can either conclude that the ideal of objectivity should be rejected, or develop a conception of objectivity that differs from the VFI.

Lacey’s characterization of value-free science and the VNT were once mainstream positions in philosophy of science. Their widespread acceptance was closely connected to Reichenbach’s famous distinction between context of discovery and context of justification . Reichenbach first made this distinction with respect to the epistemology of mathematics:

the objective relation from the given entities to the solution, and the subjective way of finding it, are clearly separated for problems of a deductive character […] we must learn to make the same distinction for the problem of the inductive relation from facts to theories. (Reichenbach 1938: 36–37)

The standard interpretation of this statement marks contextual values, which may have contributed to the discovery of a theory, as irrelevant for justifying the acceptance of a theory, and for assessing how evidence bears on theory—the relation that is crucial for the objectivity of science. Contextual values are restricted to a matter of individual psychology that may influence the discovery, development and proliferation of a scientific theory, but not its epistemic status.

This distinction played a crucial role in post-World War II philosophy of science. It presupposes, however, a clear-cut distinction between cognitive values on the one hand and contextual values on the other. While this may be prima facie plausible for disciplines such as physics, there is an abundance of contextual values in the social sciences, for instance, in the conceptualization and measurement of a nation’s wealth, or in different ways to measure the inflation rate (cf. Dupré 2007; Reiss 2008). More generally, three major lines of criticism can be identified.

First, Helen Longino (1996) has argued that traditional cognitive values such as consistency, simplicity, breadth of scope and fruitfulness are not purely cognitive or epistemic after all, and that their use imports political and social values into contexts of scientific judgment. According to her, the use of cognitive values in scientific judgments is not always, not even normally, politically neutral. She proposes to juxtapose these values with feminist values such as novelty, ontological heterogeneity, mutuality of interaction, applicability to human needs and diffusion of power, and argues that the use of the traditional value instead of its alternative (e.g., simplicity instead of ontological heterogeneity) can lead to biases and adverse research results. Longino’s argument here is different from the one discussed in section 3.1 . It casts the very distinction between cognitive and contextual values into doubt.

The second argument against the possibility of value-free science is semantic and attacks the neutrality of scientific theories: fact and value are frequently entangled because of the use of so-called “thick” ethical concepts in science (Putnam 2002)—i.e., ethical concepts that have mixed descriptive and normative content. For example, a description such as “dangerous technology” involves a value judgment about the technology and the risks it implies, but it also has a descriptive content: it is uncertain and hard to predict whether using that technology will really trigger those risks. If the use of such terms, where facts and values are inextricably entangled, is inevitable in scientific reasoning, it is impossible to describe hypotheses and results in a value-free manner, undermining the value-neutrality thesis.

Indeed, John Dupré has argued that thick ethical terms are ineliminable from science, at least certain parts of it (Dupré 2007). Dupré’s point is essentially that scientific hypotheses and results concern us because they are relevant to human interests, and thus they will necessarily be couched in a language that uses thick ethical terms. While it will often be possible to translate ethically thick descriptions into neutral ones, the translation cannot be made without losses, and these losses obtain precisely because human interests are involved (see section 6.2 for a case study from social science). According to Dupré, then, many scientific statements are value-free only because their truth or falsity does not matter to us:

Whether electrons have a positive or a negative charge and whether there is a black hole in the middle of our galaxy are questions of absolutely no immediate importance to us. The only human interests they touch (and these they may indeed touch deeply) are cognitive ones, and so the only values that they implicate are cognitive values. (2007: 31)

A third challenge to the VNT, and perhaps the most influential one, was raised first by Richard Rudner in his influential article “The Scientist Qua Scientist Makes Value Judgments” (Rudner 1953). Rudner disputes the core of the VNT and the context of discovery/justification distinction: the idea that the acceptance of a scientific theory can in principle be value-free. First, Rudner argues that

no analysis of what constitutes the method of science would be satisfactory unless it comprised some assertion to the effect that the scientist as scientist accepts or rejects hypotheses . (1953: 2)

This assumption stems from industrial quality control and other application-oriented research. In such contexts, it is often necessary to accept or to reject a hypothesis (e.g., the efficacy of a drug) in order to make effective decisions.

Second, he notes that no scientific hypothesis is ever confirmed beyond reasonable doubt—some probability of error always remains. When we accept or reject a hypothesis, there is always a chance that our decision is mistaken. Hence, our decision is also “a function of the importance , in the typically ethical sense, of making a mistake in accepting or rejecting a hypothesis” (1953: 2): we are balancing the seriousness of two possible errors (erroneous acceptance/rejection of the hypothesis) against each other. This corresponds to type I and type II error in statistical inference.

The decision to accept or reject a hypothesis involves a value judgment (at least implicitly) because scientists have to judge which of the consequences of an erroneous decision they deem more palatable: (1) some individuals die of the side effects of a drug erroneously judged to be safe; or (2) other individuals die of a condition because they did not have access to a treatment that was erroneously judged to be unsafe. Hence, ethical judgments and contextual values necessarily enter the scientist’s core activity of accepting and rejecting hypotheses, and the VNT stands refuted. Closely related arguments can be found in Churchman (1948) and Braithwaite (1953). Hempel (1965: 91–92) gives a modified account of Rudner’s argument by distinguishing between judgments of confirmation , which are free of contextual values, and judgments of acceptance . Since even strongly confirming evidence cannot fully prove a universal scientific law, we have to live with a residual “inductive risk” in inferring that law. Contextual values influence scientific methods by determining the acceptable amount of inductive risk (see also Douglas 2000).

But how general are Rudner’s objections? Apparently, his result holds true of applied science, but not necessarily of fundamental research. For the latter domain, two major lines of rebuttals have been proposed. First, Richard Jeffrey (1956) notes that lawlike hypotheses in theoretical science (e.g., the gravitational law in Newtonian mechanics) are characterized by their general scope and not confined to a particular application. Obviously, a scientist cannot fine-tune her decisions to their possible consequences in a wide variety of different contexts. So she should just refrain from the essentially pragmatic decision to accept or reject hypotheses. By restricting scientific reasoning to gathering and interpreting evidence, possibly supplemented by assessing the probability of a hypothesis, Jeffrey tries to save the VNT in fundamental scientific research, and the objectivity of scientific reasoning.

Second, Isaac Levi (1960) observes that scientists commit themselves to certain standards of inference when they become a member of the profession. This may, for example, lead to the statistical rejection of a hypothesis when the observed significance level is smaller than 5%. These community standards may eliminate any room for contextual ethical judgment on behalf of the scientist: they determine when she should accept a hypothesis as established. Value judgments may be implicit in how a scientific community sets standards of inference (compare section 5.1 ), but not in the daily work of an individual scientist (cf. Wilholt 2013).

Both defenses of the VNT focus on the impact of values in theory choice, either by denying that scientists actually choose theories (Jeffrey), or by referring to community standards and restricting the VNT to the individual scientist (Levi). Douglas (2000: 563–565) points out, however, that the “acceptance” of scientific theories is only one of several places for values to enter scientific reasoning, albeit an especially prominent and explicit one. Many decisions in the process of scientific inquiry may conceal implicit value judgments: the design of an experiment, the methodology for conducting it, the characterization of the data, the choice of a statistical method for processing and analyzing data, the interpretational process findings, etc. None of these methodological decisions could be made without consideration of the possible consequences that could occur. Douglas gives, as a case study, a series of experiments where carcinogenic effects of dioxin exposure on rats were probed. Contextual values such as safety and risk aversion affected the conducted research at various stages: first, in the classification of pathological samples as benign or cancerous (over which a lot of expert disagreement occurred), second, in the extrapolation from the high-dose experimental conditions to the more realistic low-dose conditions. In both cases, the choice of a conservative classification or model had to be weighed against the adverse consequences for society that could result from underestimating the risks (see also Biddle 2013).

These diagnoses cast a gloomy light on attempts to divide scientific labor between gathering evidence and determining the degree of confirmation (value-free) on the one hand and accepting scientific theories (value-laden) on the other. The entire process of conceptualizing, gathering and interpreting evidence is so entangled with contextual values that no neat division, as Jeffrey envisions, will work outside the narrow realm of statistical inference—and even there, doubts may be raised ( see section 4.2 ).

Philip Kitcher (2011a: 31–40; see also Kitcher 2011b) gives an alternative argument, based on his idea of “significant truths”. There are simply too many truths that are of no interest whatsoever, such as the total number of offside positions in a low-level football competition. Science, then, doesn’t aim at truth simpliciter but rather at something more narrow: truth worth pursuing from the point of view of our cognitive, practical and social goals. Any truth that is worth pursuing in this sense is what he calls a “significant truth”. Clearly, it is value judgments that help us decide whether or not any given truth is significant.

Kitcher goes on to observing that the process of scientific investigation cannot neatly be divided into a stage in which the research question is chosen, one in which the evidence is gathered and one in which a judgment about the question is made on the basis of the evidence. Rather, the sequence is multiply iterated, and at each stage, the researcher has to decide whether previous results warrant pursuit of the current line of research, or whether she should switch to another avenue. Such choices are laden with contextual values.

Values in science also interact, according to Kitcher, in a non-trivial way. Assume we endorse predictive accuracy as an important goal of science. However, there may not be a convincing strategy to reach this goal in some domain of science, for instance because that domain is characterized by strong non-linear dependencies. In this case, predictive accuracy might have to yield to achieving other values, such as consistency with theories in neighbor domains. Conversely, changing social goals lead to re-evaluations of scientific knowledge and research methods.

Science, then, cannot be value-free because no scientist ever works exclusively in the supposedly value-free zone of assessing and accepting hypotheses. Evidence is gathered and hypotheses are assessed and accepted in the light of their potential for application and fruitful research avenues. Both cognitive and contextual value judgments guide these choices and are themselves influenced by their results.

The discussion so far has focused on the VNT, the practical attainability of the VFI, but little has been said about whether a value-free science is desirable in the first place. This subsection discusses this topic with special attention to informing and advising public policy from a scientific perspective. While the VFI, and many arguments for and against it, can be applied to science as a whole, the interface of science and public policy is the place where the intrusion of values into science is especially salient, and where it is surrounded by the greatest controversy. In the 2009 “Climategate” affair, leaked emails from climate scientists raised suspicions that they were pursuing a particular socio-political agenda that affected their research in an improper way. Later inquiries and reports absolved them from charges of misconduct, but the suspicions alone did much to damage the authority of science in the public arena.

Indeed, many debates at the interface of science and public policy are characterized by disagreements on propositions that combine a factual basis with specific goals and values. Take, for instance, the view that growing transgenic crops carries too much risk in terms of biosecurity, or addressing global warming by phasing out fossil energies immediately. The critical question in such debates is whether there are theses \(T\) such that one side in the debate endorses \(T\), the other side rejects it, the evidence is shared, and both sides have good reasons for their respective positions.

According to the VFI, scientists should uncover an epistemic, value-free basis for resolving such disagreements and restrict the dissent to the realm of value judgments. Even if the VNT should turn out to be untenable, and a strict separation to be impossible, the VFI may have an important function for guiding scientific research and for minimizing the impact of values on an objective science. In the philosophy of science, one camp of scholars defends the VFI as a necessary antidote to individual and institutional interests, such as Hugh Lacey (1999, 2002), Ernan McMullin (1982) and Sandra Mitchell (2004), while others adopt a critical attitude, such as Helen Longino (1990, 1996), Philip Kitcher (2011a) and Heather Douglas (2009). These criticisms we discuss mainly refer to the desirability or the conceptual (un)clarity of the VFI.

First, it has been argued that the VFI is not desirable at all. Feminist philosophers (e.g., Harding 1991; Okruhlik 1994; Lloyd 2005) have argued that science often carries a heavy androcentric values, for instance in biological theories about sex, gender and rape. The charge against these values is not so much that they are contextual rather than cognitive, but that they are unjustified. Moreover, if scientists did follow the VFI rigidly, policy-makers would pay even less attention to them, with a detrimental effect on the decisions they take (Cranor 1993). Given these shortcomings, the VFI has to be rethought if it is supposed to play a useful role for guiding scientific research and leading to better policy decisions. Section 4.3 and section 5.2 elaborate on this line of criticism in the context of scientific community practices, and a science in the service of society.

Second, the autonomy of science often fails in practice due to the presence of external stakeholders, such as funding agencies and industry lobbies. To save the epistemic authority of science, Douglas (2009: 7–8) proposes to detach it from its autonomy by reformulating the VFI and distinguishing between direct and indirect roles of values in science . Contextual values may legitimately affect the assessment of evidence by indicating the appropriate standard of evidence, the representation of complex processes, the severity of consequences of a decision, the interpretation of noisy datasets, and so on (see also Winsberg 2012). This concerns, above all, policy-related disciplines such as climate science or economics that routinely perform scientific risk analyses for real-world problems (cf. also Shrader-Frechette 1991). Values should, however, not be “reasons in themselves”, that is, evidence or defeaters for evidence (direct role, illegitimate) and as “helping to decide what should count as a sufficient reason for a choice” (indirect role, legitimate). This prohibition for values to replace or dismiss scientific evidence is called detached objectivity by Douglas, but it is complemented by various other aspects that relate to a reflective balancing of various perspectives and the procedural, social aspects of science (2009: ch. 6).

That said, Douglas’ proposal is not very concrete when it comes to implementation, e.g., regarding the way diverse values should be balanced. Compromising in the middle cannot be the solution (Weber 1917 [1949]). First, no standpoint is, just in virtue of being in the middle, evidentially supported vis-à-vis more extreme positions. Second, these middle positions are also, from a practical point of view, the least functional when it comes to advising policy-makers.

Moreover, the distinction between direct and indirect roles of values in science may not be sufficiently clear-cut to police the legitimate use of values in science, and to draw the necessary borderlines. Assume that a scientist considers, for whatever reason, the consequences of erroneously accepting hypothesis \(H\) undesirable. Therefore he uses a statistical model whose results are likely to favor ¬\(H\) over \(H\). Is this a matter of reasonable conservativeness? Or doesn’t it amount to reasoning to a foregone conclusion, and to treating values as evidence (cf. Elliott 2011: 320–321)?

The most recent literature on values and evidence in science presents us with a broad spectrum of opinions. Steele (2012) and Winsberg (2012) agree that probabilistic assessments of uncertainty involve contextual value judgments. While Steele defends this point by analyzing the role of scientists as policy advisors, Winsberg points to the influence of contextual values in the selection and representation of physical processes in climate modeling. Betz (2013) argues, by contrast, that scientists can largely avoid making contextual value judgments if they carefully express the uncertainty involved with their evidential judgments, e.g., by using a scale ranging from purely qualitative evidence (such as expert judgment) to precise probabilistic assessments. The issue of value judgments at earlier stages of inquiry is not addressed by this proposal; however, disentangling evidential judgments and judgments involving contextual values at the stage of theory assessment may be a good thing in itself.

Thus, should we or should we not worried about values in scientific reasoning? While the interplay of values and evidential considerations need not be pernicious, it is unclear why it adds to the success or the authority of science. How are we going to ensure that the permissive attitude towards values in setting evidential standards etc. is not abused? In the absence of a general theory about which contextual values are beneficial and which are pernicious, the VFI might as well be as a first-order approximation to a sound, transparent and objective science.

4. Objectivity as Freedom from Personal Biases

This section deals with scientific objectivity as a form of intersubjectivity—as freedom from personal biases. According to this view, science is objective to the extent that personal biases are absent from scientific reasoning, or that they can be eliminated in a social process. Perhaps all science is necessarily perspectival. Perhaps we cannot sensibly draw scientific inferences without a host of background assumptions, which may include assumptions about values. Perhaps all scientists are biased in some way. But objective scientific results do not, or so the argument goes, depend on researchers’ personal preferences or experiences—they are the result of a process where individual biases are gradually filtered out and replaced by agreed upon evidence. That, among other things, is what distinguishes science from the arts and other human activities, and scientific knowledge from a fact-independent social construction (e.g., Haack 2003).

Paradigmatic ways to achieve objectivity in this sense are measurement and quantification. What has been measured and quantified has been verified relative to a standard. The truth, say, that the Eiffel Tower is 324 meters tall is relative to a standard unit and conventions about how to use certain instruments, so it is neither aperspectival nor free from assumptions, but it is independent of the person making the measurement.

We will begin with a discussion of objectivity, so conceived, in measurement, discuss the ideal of “mechanical objectivity” and then investigate to what extent freedom from personal biases can be implemented in statistical evidence and inductive inference—arguably the core of scientific reasoning, especially in quantitatively oriented sciences. Finally, we discuss Feyerabend’s radical criticism of a rational scientific method that can be mechanically applied, and his defense of the epistemic and social benefits of personal “bias” and idiosyncrasy.

Measurement is often thought to epitomize scientific objectivity, most famously captured in Lord Kelvin’s dictum

when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science , whatever the matter may be. (Kelvin 1883, 73)

Measurement can certainly achieve some independence of perspective. Yesterday’s weather in Durham UK may have been “really hot” to the average North Eastern Brit and “very cold” to the average Mexican, but they’ll both accept that it was 21°C. Clearly, however, measurement does not result in a “view from nowhere”, nor are typical measurement results free from presuppositions. Measurement instruments interact with the environment, and so results will always be a product of both the properties of the environment we aim to measure as well as the properties of the instrument. Instruments, thus, provide a perspectival view on the world (cf. Giere 2006).

Moreover, making sense of measurement results requires interpretation. Consider temperature measurement. Thermometers function by relating an unobservable quantity, temperature, to an observable quantity, expansion (or length) of a fluid or gas in a glass tube; that is, thermometers measure temperature by assuming that length is a function of temperature: length = \(f\)(temperature). The function \(f\) is not known a priori , and it cannot be tested either (because it could in principle only be tested using a veridical thermometer, and the veridicality of the thermometer is just what is at stake here). Making a specific assumption, for instance that \(f\) is linear, solves that problem by fiat. But this “solution” does not take us very far because different thermometric substances (e.g., mercury, air or water) yield different results for the points intermediate between the two fixed points 0°C and 100°C, and so they can’t all expand linearly.

According to Hasok Chang’s account of early thermometry (Chang 2004), the problem was eventually solved by using a “principle of minimalist overdetermination”, the goal of which was to find a reliable thermometer while making as few substantial assumptions (e.g., about the form for \(f\)) as possible. It was argued that if a thermometer was to be reliable, different tokens of the same thermometer type should agree with each other, and the results of air thermometers agreed the most. “Minimal” doesn’t mean zero, however, and indeed this procedure makes an important presupposition (in this case a metaphysical assumption about the one-valuedness of a physical quantity). Moreover, the procedure yielded at best a reliable instrument, not necessarily one that was best at tracking the uniquely real temperature (if there is such a thing).

What Chang argues about early thermometry is true of measurements more generally: they are always made against a backdrop of metaphysical presuppositions, theoretical expectations and other kinds of belief. Whether or not any given procedure is regarded as adequate depends to a large extent on the purposes pursued by the individual scientist or group of scientists making the measurements. Especially in the social sciences, this often means that measurement procedures are laden with normative assumptions, i.e., values.

Julian Reiss (2008, 2013) has argued that economic indicators such as consumer price inflation, gross domestic product and the unemployment rate are value-laden in this sense. Consumer-price indices, for instance, assume that if a consumer prefers a bundle \(x\) over an alternative \(y\), then \(x\) is better for her than \(y\), which is as ethically charged as it is controversial. National income measures assume that nations that exchange a larger share of goods and services on markets are richer than nations where the same goods and services are provided by the government or within households, which too is ethically charged and controversial.

While not free of assumptions and values, the goal of many measurement procedures remains to reduce the influence of personal biases and idiosyncrasies. The Nixon administration, famously, indexed social security payments to the consumer-price index in order to eliminate the dependence of security recipients on the flimsiest of party politics: to make increases automatic instead of a result of political negotiations (Nixon 1969). Lorraine Daston and Peter Galison refer to this as mechanical objectivity . They write:

Finally, we come to the full-fledged establishment of mechanical objectivity as the ideal of scientific representation. What we find is that the image, as standard bearer of is objectivity is tied to a relentless search to replace individual volition and discretion in depiction by the invariable routines of mechanical reproduction. (Daston and Galison 1992: 98)

Mechanical objectivity reduces the importance of human contributions to scientific results to a minimum, and therefore enables science to proceed on a large scale where bonds of trust between individuals can no longer hold (Daston 1992). Trust in mechanical procedures thus replaces trust in individual scientists.

In his book Trust in Numbers , Theodore Porter pursues this line of thought in great detail. In particular, on the basis of case studies involving British actuaries in the mid-nineteenth century, of French state engineers throughout the century, and of the US Army Corps of Engineers from 1920 to 1960, he argues for two causal claims. First, measurement instruments and quantitative procedures originate in commercial and administrative needs and affect the ways in which the natural and social sciences are practiced, not the other way around. The mushrooming of instruments such as chemical balances, barometers, chronometers was largely a result of social pressures and the demands of democratic societies. Administering large territories or controlling diverse people and processes is not always possible on the basis of personal trust and thus “objective procedures” (which do not require trust in persons) took the place of “subjective judgments” (which do). Second, he argues that quantification is a technology of distrust and weakness, and not of strength. It is weak administrators who do not have the social status, political support or professional solidarity to defend their experts’ judgments. They therefore subject decisions to public scrutiny, which means that they must be made in a publicly accessible form.

This is the situation in which scientists who work in areas where the science/policy boundary is fluid find themselves:

The National Academy of Sciences has accepted the principle that scientists should declare their conflicts of interest and financial holdings before offering policy advice, or even information to the government. And while police inspections of notebooks remain exceptional, the personal and financial interests of scientists and engineers are often considered material, especially in legal and regulatory contexts. Strategies of impersonality must be understood partly as defenses against such suspicions […]. Objectivity means knowledge that does not depend too much on the particular individuals who author it. (Porter 1995: 229)

Measurement and quantification help to reduce the influence of personal biases and idiosyncrasies and they reduce the need to trust the scientist or government official, but often at a cost. Standardizing scientific procedures becomes difficult when their subject matters are not homogeneous, and few domains outside fundamental physics are. Attempts to quantify procedures for treatment and policy decisions that we find in evidence-based practices are currently transferred to a variety of sciences such as medicine, nursing, psychology, education and social policy. However, they often lack a certain degree of responsiveness to the peculiarities of their subjects and the local conditions to which they are applied (see also section 5.3 ).

Moreover, the measurement and quantification of characteristics of scientific interest is only half of the story. We also want to describe relations between the quantities and make inferences using statistical analysis. Statistics thus helps to quantify further aspects of scientific work. We will now examine whether or not statistical analysis can proceed in a way free from personal biases and idiosyncrasies—for more detail, see the entry on philosophy of statistics .

4.2 Statistical Evidence

The appraisal of scientific evidence is traditionally regarded as a domain of scientific reasoning where the ideal of scientific objectivity has strong normative force, and where it is also well-entrenched in scientific practice. Episodes such as Galilei’s observations of the Jupiter moons, Lavoisier’s calcination experiments, and Eddington’s observation of the 1919 eclipse are found in all philosophy of science textbooks because they exemplify how evidence can be persuasive and compelling to scientists with different backgrounds. The crucial question is therefore: can we identify an “objective” concept of scientific evidence that is independent of the personal biases of the experimenter and interpreter?

Inferential statistics—the field that investigates the validity of inferences from data to theory—tries to answer this question. It is extremely influential in modern science, pervading experimental research as well as the assessment and acceptance of our most fundamental theories. For instance, a statistical argument helped to establish the recent discovery of the Higgs Boson. We now compare the main theories of statistical evidence with respect to the objectivity of the claims they produce. They mainly differ with respect to the role of an explicitly subjective interpretation of probability.

Bayesian inference quantifies scientific evidence by means of probabilities that are interpreted as a scientist’s subjective degrees of belief. The Bayesian thus leaves behind Carnap’s (1950) idea that probability is determined by a logical relation between sentences. For example, the prior degree of belief in hypothesis \(H\), written \(p(H)\), can in principle take any value in the interval \([0,1]\). Simultaneously held degrees of belief in different hypotheses are, however, constrained by the laws of probability. After learning evidence E, the degree of belief in \(H\) is changed from its prior probability \(p(H)\) to the conditional degree of belief \(p(H \mid E)\), commonly called the posterior probability of \(H\). Both quantities can be related to each other by means of Bayes’ Theorem .

These days, the Bayesian approach is extremely influential in philosophy and rapidly gaining ground across all scientific disciplines. For quantifying evidence for a hypothesis, Bayesian statisticians almost uniformly use the Bayes factor , that is, the ratio of prior to posterior odds in favor of a hypothesis. The Bayes factor in favor of hypothesis \(H\) against its negation \(\neg\)\(H\) in the light of evidence \(E\) can be written as

or in other words, as the likelihood ratio between \(H\) and \(\neg\)\(H\). The Bayes factor reduces to the likelihoodist conception of evidence (Royall 1997) for the case of two competing point hypotheses. For further discussion of Bayesian measures of evidence, see Good (1950), Sprenger and Hartmann (2019: ch. 1) and the entry on confirmation and evidential support .

Unsurprisingly, the idea to measure scientific evidence in terms of subjective probability has met resistance. For example, the statistician Ronald A. Fisher (1935: 6–7) has argued that measuring psychological tendencies cannot be relevant for scientific inquiry and sustain claims to objectivity. Indeed, how should scientific objectivity square with subjective degree of belief? Bayesians have responded to this challenge in various ways:

Howson (2000) and Howson and Urbach (2006) consider the objection misplaced. In the same way that deductive logic does not judge the correctness of the premises but just advises you what to infer from them, Bayesian inductive logic provides rational rules for representing uncertainty and making inductive inferences. Choosing the premises (e.g., the prior distributions) “objectively” falls outside the scope of Bayesian analysis.

Convergence or merging-of-opinion theorems guarantee that under certain circumstances, agents with very different initial attitudes who observe the same evidence will obtain similar posterior degrees of belief in the long run. However, they are asymptotic results without direct implications for inference with real-life datasets (see also Earman 1992: ch. 6). In such cases, the choice of the prior matters, and it may be beset with idiosyncratic bias and manifest social values.

Adopting a more modest stance, Sprenger (2018) accepts that Bayesian inference does not achieve the goal of objectivity in the sense of intersubjective agreement (concordant objectivity), or being free of personal values, bias and subjective judgment. However, he argues that competing schools of inference such as frequentist inference face this problem to the same degree, perhaps even worse. Moreover, some features of Bayesian inference (e.g., the transparency about prior assumptions) fit recent, socially oriented conceptions of objectivity that we discuss in section 5 .

A radical Bayesian solution to the problem of personal bias is to adopt a principle that radically constrains an agent’s rational degrees of belief, such as the Principle of Maximum Entropy (MaxEnt—Jaynes 1968; Williamson 2010). According to MaxEnt, degrees of belief must be probabilistic and in sync with empirical constraints, but conditional on these constraints, they must be equivocal, that is, as middling as possible. This latter constraint amounts to maximizing the entropy of the probability distribution in question. The MaxEnt approach eliminates various sources of subjective bias at the expense of narrowing down the range of rational degrees of belief. An alternative objective Bayesian solution consists in so-called “objective priors” : prior probabilities that do not represent an agent’s factual attitudes, but are determined by principles of symmetry, mathematical convenience or maximizing the influence of the data on the posterior (e.g., Jeffreys 1939 [1980]; Bernardo 2012).

Thus, Bayesian inference, which analyzes statistical evidence from the vantage point of rational belief, provides only a partial answer to securing scientific objectivity from personal idiosyncrasy.

The frequentist conception of evidence is based on the idea of the statistical test of a hypothesis . Under the influence of the statisticians Jerzy Neyman and Egon Pearson, tests were often regarded as rational decision procedures that minimize the relative frequency of wrong decisions in a hypothetical series of repetitions of a test (hence the name “frequentism”). Rudner’s argument in section 3.2 has pointed out the limits of this conception of hypothesis tests: the choice of thresholds for acceptance and rejection (i.e., the acceptable type I and II error rates) may reflect contextual value judgments and personal bias. Moreover, the losses associated with erroneously accepting or rejecting that hypothesis depend on the context of application which may be unbeknownst to the experimenter.

Alternatively, scientists can restrict themselves to a purely evidential interpretation of hypothesis tests and leave decisions to policy-makers and regulatory agencies. The statistician and biologist R.A. Fisher (1935, 1956) proposed what later became the orthodox quantification of evidence in frequentist statistics. Suppose a “null” or default hypothesis \(H_0\) denotes that an intervention has zero effect. If the observed data are “extreme” under \(H_0\)—i.e., if it was highly likely to observe a result that agrees better with \(H_0\)—the data provide evidence against the null hypothesis and for the efficacy of the intervention. The epistemological rationale is connected to the idea of severe testing (Mayo 1996): if the intervention were ineffective, we would, in all likelihood, have found data that agree better with the null hypothesis. The strength of evidence against \(H_0\) is equal to the \(p\)-value : the lower it is, the stronger evidence \(E\) speaks against the null hypothesis \(H_0\).

Unlike Bayes factors, this concept of statistical evidence does not depend on personal degrees of belief. However, this does not necessarily mean that \(p\)-values are more objective. First, \(p\)-values are usually classified as “non-significant” (\(p > .05\)), “significant” (\(p < .05\)), “highly significant”, and so on. Not only that these thresholds and labels are largely arbitrary, they also promote publication bias : non-significant findings are often classified as “failed studies” (i.e., the efficacy of the intervention could not be shown), rarely published and end up in the proverbial “file drawer”. Much valuable research is suppressed. Conversely, significant findings may often occur when the null hypothesis is actually true, especially when researchers have been “hunting for significance”. In fact, researchers have an incentive to keep their \(p\)-values low: the stronger the evidence, the more convincing the narrative, the greater the impact—and the higher the chance for a good publication and career-relevant rewards. Moving the goalpost by “p-hacking” outcomes—for example by eliminating outliers, selective reporting or restricting the analysis to a subgroup—evidently biases the research results and compromises the objectivity of experimental research.

In particular, such questionable research practices (QRP) increase the type I error rate, which measures the rate at which false hypotheses are accepted, substantially over its nominal 5% level and contribute to publication bias (Bakker et al. 2012). Ioannidis (2005) concludes that “most published research findings are false”—they are the combined result of a low base rate of effective causal interventions, the file drawer effect and the widespread presence of questionable research practices. The frequentist logic of hypothesis testing aggravates the problem because it provides a framework where all these biases can easily enter (Ziliak and McCloskey 2008; Sprenger 2016). These radical conclusions are also confirmed by empirical findings: in many disciplines researchers fail to replicate findings by other scientific teams. See section 5.1 for more detail.

Summing up our findings, neither of the two major frameworks of statistical inference manages to eliminate all sources of personal bias and idiosyncrasy. The Bayesian considers subjective assumptions to be an irreducible part of scientific reasoning and sees no harm in making them explicit. The frequentist conception of evidence based on \(p\)-values avoids these explicitly subjective elements, but at the price of a misleading impression of objectivity and frequent abuse in practice. A defense of frequentist inference should, in our opinion, stress that the relatively rigid rules for interpreting statistical evidence facilitate communication and assessment of research results in the scientific community—something that is harder to achieve for a Bayesian. We now turn from specific methods for stating and interpreting evidence to a radical criticism of the idea that there is a rational scientific method.

In his writings of the 1970s, Paul Feyerabend launched a profound attack on the rationality and objectivity of scientific method. His position is exceptional in the philosophical literature since traditionally, the threat for objective and successful science is located in contextual rather than epistemic values. Feyerabend turns this view upside down: it is the “tyranny” of rational method, and the emphasis on epistemic rather than contextual values that prevents us from having a science in the service of society. Moreover, he welcomes a diversity of different personal, also idiosyncratic perspectives, thus denying the idea that freedom from personal “bias” is epistemically and socially beneficial.

The starting point of Feyerabend’s criticism of rational method is the thesis that strict epistemic rules such as those expressed by the VFI only suppress an open exchange of ideas, extinguish scientific creativity and prevent a free and truly democratic science. In his classic “Against Method” (1975: chs. 8–13), Feyerabend elaborates on this criticism by examining a famous episode in the history of science. When the Catholic Church objected to Galilean mechanics, it had the better arguments by the standards of seventeenth-century science. Their conservatism in their position was scientifically backed: Galilei’s telescopes were unreliable for celestial observations, and many well-established phenomena (no fixed star parallax, invariance of laws of motion) could not yet be explained in the heliocentric system. With hindsight, Galilei managed to achieve groundbreaking scientific progress just because he deliberately violated rules of scientific reasoning. Hence Feyerabend’s dictum “Anything goes”: no methodology whatsoever is able to capture the creative and often irrational ways by which science deepens our understanding of the world. Good scientific reasoning cannot be captured by rational method, as Carnap, Hempel and Popper postulated.

The drawbacks of an objective, value-free and method-bound view on science and scientific method are not only epistemic. Such a view narrows down our perspective and makes us less free, open-minded, creative, and ultimately, less human in our thinking (Feyerabend 1975: 154). It is therefore neither possible nor desirable to have an objective, value-free science (cf. Feyerabend 1978: 78–79). As a consequence, Feyerabend sees traditional forms of inquiry about our world (e.g., Chinese medicine) on a par with their Western competitors. He denounces appeals to “objective” standards as rhetorical tools for bolstering the epistemic authority of a small intellectual elite (=Western scientists), and as barely disguised statements of preference for one’s own worldview:

there is hardly any difference between the members of a “primitive” tribe who defend their laws because they are the laws of the gods […] and a rationalist who appeals to “objective” standards, except that the former know what they are doing while the latter does not. (1978: 82)

In particular, when discussing other traditions, we often project our own worldview and value judgments into them instead of making an impartial comparison (1978: 80–83). There is no purely rational justification for dismissing other perspectives in favor of the Western scientific worldview—the insistence on our Western approach may be as justified as insisting on absolute space and time after the Theory of Relativity.

The Galilei example also illustrates that personal perspective and idiosyncratic “bias” need not be bad for science. Feyerabend argues further that scientific research is accountable to society and should be kept in check by democratic institutions, and laymen in particular. Their particular perspectives can help to determine the funding agenda and to set ethical standards for scientific inquiry, but also be useful for traditionally value-free tasks such as choosing an appropriate research method and assessing scientific evidence. Feyerabend’s writings on this issue were much influenced by witnessing the Civil Rights Movement in the U.S. and the increasing emancipation of minorities, such as Blacks, Asians and Hispanics.

All this is not meant to say that truth loses its function as a normative concept, nor that all scientific claims are equally acceptable. Rather, Feyerabend advocates an epistemic pluralism that accepts diverse approaches to acquiring knowledge. Rather than defending a narrow and misleading ideal of objectivity, science should respect the diversity of values and traditions that drive our inquiries about the world (1978: 106–107). This would put science back into the role it had during the scientific revolution or the Enlightenment: as a liberating force that fought intellectual and political oppression by the sovereign, the nobility or the clergy. Objections to this view are discussed at the end of section 5.2 .

5. Objectivity as a Feature of Scientific Communities and Their Practices

This section addresses various accounts that regard scientific objectivity essentially as a function of social practices in science and the social organization of the scientific community. All these accounts reject the characterization of scientific objectivity as a function of correspondence between theories and the world, as a feature of individual reasoning practices, or as pertaining to individual studies and experiments (see also Douglas 2011). Instead, they evaluate the objectivity of a collective of studies, as well as the methods and community practices that structure and guide scientific research. More precisely, they adopt a meta-analytic perspective for assessing the reliability of scientific results (section 5.1), and they construct objectivity from a feminist perspective: as an open interchange of mutual criticism, or as being anchored in the “situatedness” of our scientific practices and the knowledge we gain ( section 5.2 ).

The collectivist perspective is especially useful when an entire discipline enters a stage of crisis: its members become convinced that a significant proportion of findings are not trustworthy. A contemporary example of such a situation is the replication crisis , which was briefly mentioned in the previous section and concerns the reproducibility of scientific knowledge claims in a variety of different fields (most prominently: psychology, biology, medicine). Large-scale replication projects have noticed that many findings which we considered as an integral part of scientific knowledge failed to replicate in settings that were designed to mimic the original experiment as closely as possible (e.g., Open Science Collaboration 2015). Successful attempts at replicating an experimental result have long been argued to provide evidence of freedom from particular kinds of artefacts and thus the trustworthiness of the result. Compare the entry on experiment in physics . Likewise, failure to replicate indicates that either the original finding, the result of the replication attempt, or both, are biased—though see John Norton’s (ms., ch. 3—see Other Internet Resources) arguments that the evidential value of (failed) replications crucially depends on researchers’ material background assumptions.

When replication failures in a discipline are particularly significant, one may conclude that the published literature lacks objectivity—at a minimum the discipline fails to inspire trust that its findings are more than artefacts of the researchers’ efforts. Conversely, when observed effects can be replicated in follow-up experiments, a kind of objectivity is reached that goes beyond the ideas of freedom from personal bias, mechanical objectivity, and subject-independent measurement, discussed in section 4.1 .

Freese and Peterson (2018) call this idea statistical objectivity . It grounds in the view that even the most scrupulous and diligent researchers cannot achieve full objectivity all by themselves. The term “objectivity” instead applies to a collection or population of studies, with meta-analysis (a formal method for aggregating the results from ranges of studies) as the “apex of objectivity” (Freese and Peterson 2018, 304; see also Stegenga 2011, 2018). In particular, aggregating studies from different researchers may provide evidence of systematic bias and questionable research practices (QRP) in the published literature. This diagnostic function of meta-analysis for detecting violations of objectivity is enhanced by statistical techniques such as the funnel plot and the \(p\)-curve (Simonsohn et al. 2014).

Apart from this epistemic dimension, research on statistical objectivity also has an activist dimension: methodologists urge researchers to make publicly available essential parts of their research before the data analysis starts, and to make their methods and data sources more transparent. For example, it is conjectured that the replicability (and thus objectivity) of science will increase by making all data available online, by preregistering experiments, and by using the registered reports model for journal articles (i.e., the journal decides on publication before data collection on the basis of the significance of the proposed research as well as the experimental design). The idea is that transparency about the data set and the experimental design will make it easier to stage a replication of an experiment and to assess its methodological quality. Moreover, publicly committing to a data analysis plan beforehand will lower the rate of QRPs and of attempts to accommodate data to hypotheses rather than making proper predictions.

All in all, statistical objectivity moves the discussion of objectivity to the level of population of studies. There, it takes up and modifies several conceptions of objectivity that we have seen before: most prominently, freedom of subjective bias, which is replaced with collective bias and pernicious conventions, and the subject-independent measurement of a physical quantity, which is replaced by reproducibility of effects.

Traditional notions of objectivity as faithfulness to facts or freedom of contextual values have also been challenged from a feminist perspective. These critiques can be grouped in three major research programs: feminist epistemology, feminist standpoint theory and feminist postmodernism (Crasnow 2013). The program of feminist epistemology explores the impact of sex and gender on the production of scientific knowledge. More precisely, feminist epistemology highlights the epistemic risks resulting from the systematic exclusion of women from the ranks of scientists, and the neglect of women as objects of study. Prominent case studies are the neglect of female orgasm in biology, testing medical drugs on male participants only, focusing on male specimen when studying the social behavior of primates, and explaining human mating patterns by means of imaginary neolithic societies (e.g., Hrdy 1977; Lloyd 1993, 2005). See also the entry on feminist philosophy of biology .

Often but not always, feminist epistemologists go beyond pointing out what they regard as androcentric bias and reject the value-free ideal altogether—with an eye on the social and moral responsibility of scientific inquiry. They try to show that a value-laden science can also meet important criteria for being epistemically reliable and objective (e.g., Anderson 2004; Kourany 2010). A classical representative of such efforts is Longino’s (1990) contextual empiricism . She reinforces Popper’s insistence that “the objectivity of scientific statements lies in the fact that they can be inter-subjectively tested” (1934 [2002]: 22), but unlike Popper, she conceives scientific knowledge essentially as a social product. Thus, our conception of scientific objectivity must directly engage with the social process that generates knowledge. Longino assigns a crucial function to social systems of criticism in securing the epistemic success of science. Specifically, she develops an epistemology which regards a method of inquiry as “objective to the degree that it permits transformative criticism ” (Longino 1990: 76). For an epistemic community to achieve transformative criticism, there must be:

avenues for criticism : criticism is an essential part of scientific institutions (e.g., peer review);

shared standards : the community must share a set of cognitive values for assessing theories (more on this in section 3.1 );

uptake of criticism : criticism must be able to transform scientific practice in the long run;

equality of intellectual authority : intellectual authority must be shared equally among qualified practitioners.

Longino’s contextual empiricism can be understood as a development of John Stuart Mill’s view that beliefs should never be suppressed, independently of whether they are true or false. Even the most implausible beliefs might be true, and even if they are false, they might contain a grain of truth which is worth preserving or helps to better articulate true beliefs (Mill 1859 [2003: 72]). The underlying intuition is supported by recent empirical research on the epistemic benefits of a diversity of opinions and perspectives (Page 2007). By stressing the social nature of scientific knowledge, and the importance of criticism (e.g., with respect to potential androcentric bias and inclusive practice), Longino’s account fits into the broader project of feminist epistemology.

Standpoint theory undertakes a more radical attack on traditional scientific objectivity. This view develops Marxist ideas to the effect that epistemic position is related to, and a product of, social position. Feminist standpoint theory builds on these ideas but focuses on gender, racial and other social relations. Feminist standpoint theorists and proponents of “situated knowledge” such as Donna Haraway (1988), Sandra Harding (1991, 2015a, 2015b) and Alison Wylie (2003) deny the internal coherence of a view from nowhere: all human knowledge is at base human knowledge and therefore necessarily perspectival. But they argue more than that. Not only is perspectivality the human condition, it is also a good thing to have. This is because perspectives, especially the perspectives of underprivileged classes and groups in society, come along with epistemic benefits. These ideas are controversial but they draw attention to the possibility that attempts to rid science of perspectives might not only be futile but also costly: they prevent scientists from having the epistemic benefits certain standpoints afford and from developing knowledge for marginalized groups in society. The perspectival stance can also explain why criteria for objectivity often vary with context: the relative importance of epistemic virtues is a matter of goals and interests—in other words, standpoint.

By endorsing a perspectival stance, feminist standpoint theory rejects classical elements of scientific objectivity such as neutrality and impartiality (see section 3.1 above). This is a notable difference to feminist epistemology, which is in principle (though not always in practice) compatible with traditional views of objectivity. Feminist standpoint theory is also a political project. For example, Harding (1991, 1993) demands that scientists, their communities and their practices—in other words, the ways through which knowledge is gained—be investigated as rigorously as the object of knowledge itself. This idea she refers to as “strong objectivity” replaces the “weak” conception of objectivity in the empiricist tradition: value-freedom, impartiality, rigorous adherence to methods of testing and inference. Like Feyerabend, Harding integrates a transformation of epistemic standards in science into a broader political project of rendering science more democratic and inclusive. On the other hand, she is exposed to similar objections (see also Haack 2003). Isn’t it grossly exaggerated to identify class, race and gender as important factors in the construction of physical theories? Doesn’t the feminist approach—like social constructivist approaches—lose sight of the particular epistemic qualities of science? Should non-scientists really have as much authority as trained scientists? To whom does the condition of equally shared intellectual authority apply? Nor is it clear—especially in times of fake news and filter bubbles—whether it is always a good idea to subject scientific results to democratic approval. There is no guarantee (arguably there are few good reasons to believe) that democratized or standpoint-based science leads to more reliable theories, or better decisions for society as a whole.

6. Issues in the Special Sciences

So far everything we discussed was meant to apply across all or at least most of the sciences. In this section we will look at a number of specific issues that arise in the social sciences, in economics, and in evidence-based medicine.

There is a long tradition in the philosophy of social science maintaining that there is a gulf in terms of both goals as well as methods between the natural and the social sciences. This tradition, associated with thinkers such as the neo-Kantians Heinrich Rickert and Wilhelm Windelband, the hermeneuticist Wilhelm Dilthey, the sociologist-economist Max Weber, and the twentieth-century hermeneuticists Hans-Georg Gadamer and Michael Oakeshott, holds that unlike the natural sciences whose aim it is to establish natural laws and which proceed by experimentation and causal analysis, the social sciences seek understanding (“ Verstehen ”) of social phenomena, the interpretive examination of the meanings individuals attribute to their actions (Weber 1904 [1949]; Weber 1917 [1949]; Dilthey 1910 [1986]; Windelband 1915; Rickert 1929; Oakeshott 1933; Gadamer 1960 [1989]). See also the entries on hermeneutics and Max Weber .

Understood this way, social science lacks objectivity in more than one sense. One of the more important debates concerning objectivity in the social sciences concerns the role value judgments play and, importantly, whether value-laden research entails claims about the desirability of actions. Max Weber held that the social sciences are necessarily value laden. However, they can achieve some degree of objectivity by keeping out the social researcher’s views about whether agents’ goals are commendable. In a similar vein, contemporary economics can be said to be value laden because it predicts and explains social phenomena on the basis of agents’ preferences. Nevertheless, economists are adamant that economists are not in the business of telling people what they ought to value. Modern economics is thus said to be objective in the Weberian sense of “absence of researchers’ values” —a conception that we discussed in detail in section 3 .

In his widely cited essay “‘Objectivity’ in Social Science and Social Policy” (Weber 1904 [1949]), Weber argued that the idea of an aperspectival social science was meaningless:

There is no absolutely objective scientific analysis of […] “social phenomena” independent of special and “one-sided” viewpoints according to which expressly or tacitly, consciously or unconsciously they are selected, analyzed and organized for expository purposes. (1904 [1949: 72]) All knowledge of cultural reality, as may be seen, is always knowledge from particular points of view. (1904 [1949:. 81])

The reason for this is twofold. First, social reality is too complex to admit of full description and explanation. So we have to select. But, perhaps in contraposition to the natural sciences, we cannot just select those aspects of the phenomena that fall under universal natural laws and treat everything else as “unintegrated residues” (1904 [1949: 73]). This is because, second, in the social sciences we want to understand social phenomena in their individuality, that is, in their unique configurations that have significance for us.

Values solve a selection problem. They tell us what research questions we ought to address because they inform us about the cultural importance of social phenomena:

Only a small portion of existing concrete reality is colored by our value-conditioned interest and it alone is significant to us. It is significant because it reveals relationships which are important to use due to their connection with our values. (1904 [1949: 76])

It is important to note that Weber did not think that social and natural science were different in kind, as Dilthey and others did. Social science too examines the causes of phenomena of interest, and natural science too often seeks to explain natural phenomena in their individual constellations. The role of causal laws is different in the two fields, however. Whereas establishing a causal law is often an end in itself in the natural sciences, in the social sciences laws play an attenuated and accompanying role as mere means to explain cultural phenomena in their uniqueness.

Nevertheless, for Weber social science remains objective in at least two ways. First, once research questions of interest have been settled, answers about the causes of culturally significant phenomena do not depend on the idiosyncrasies of an individual researcher:

But it obviously does not follow from this that research in the cultural sciences can only have results which are “subjective” in the sense that they are valid for one person and not for others. […] For scientific truth is precisely what is valid for all who seek the truth. (Weber 1904 [1949: 84], emphasis original)

The claims of social science can therefore be objective in our third sense ( see section 4 ). Moreover, by determining that a given phenomenon is “culturally significant” a researcher reflects on whether or not a practice is “meaningful” or “important”, and not whether or not it is commendable: “Prostitution is a cultural phenomenon just as much as religion or money” (1904 [1949: 81]). An important implication of this view came to the fore in the so-called “ Werturteilsstreit ” (quarrel concerning value judgments) of the early 1900s. In this debate, Weber maintained against the “socialists of the lectern” around Gustav Schmoller the position that social scientists qua scientists should not be directly involved in policy debates because it was not the aim of science to examine the appropriateness of ends. Given a policy goal, a social scientist could make recommendations about effective strategies to reach the goal; but social science was to be value-free in the sense of not taking a stance on the desirability of the goals themselves. This leads us to our conception of objectivity as freedom from value judgments.

Contemporary mainstream economists hold a view concerning objectivity that mirrors Max Weber’s (see above). On the one hand, it is clear that value judgments are at the heart of economic theorizing. “Preferences” are a key concept of rational choice theory, the main theory in contemporary mainstream economics. Preferences are evaluations. If an individual prefers \(A\) to \(B\), she values \(A\) higher than \(B\) (Hausman 2012). Thus, to the extent that economists predict and explain market behavior in terms of rational choice theory, they predict and explain market behavior in a way laden with value judgments.

However, economists are not themselves supposed to take a stance about whether or not whatever individuals value is also “objectively” good in a stronger sense:

[…] that an agent is rational from [rational choice theory]’s point of view does not mean that the course of action she will choose is objectively optimal. Desires do not have to align with any objective measure of “goodness”: I may want to risk swimming in a crocodile-infested lake; I may desire to smoke or drink even though I know it harms me. Optimality is determined by the agent’s desires, not the converse. (Paternotte 2011: 307–8)

In a similar vein, Gul and Pesendorfer write:

However, standard economics has no therapeutic ambition, i.e., it does not try to evaluate or improve the individual’s objectives. Economics cannot distinguish between choices that maximize happiness, choices that reflect a sense of duty, or choices that are the response to some impulse. Moreover, standard economics takes no position on the question of which of those objectives the agent should pursue. (Gul and Pesendorfer 2008: 8)

According to the standard view, all that rational choice theory demands is that people’s preferences are (internally) consistent; it has no business in telling people what they ought to prefer, whether their preferences are consistent with external norms or values. Economics is thus value-laden, but laden with the values of the agents whose behavior it seeks to predict and explain and not with the values of those who seek to predict and explain this behavior.

Whether or not social science, and economics in particular, can be objective in this—Weber’s and the contemporary economists’—sense is controversial. On the one hand, there are some reasons to believe that rational choice theory (which is at work not only in economics but also in political science and other social sciences) cannot be applied to empirical phenomena without referring to external norms or values (Sen 1993; Reiss 2013).

On the other hand, it is not clear that economists and other social scientists qua social scientists shouldn’t participate in a debate about social goals. For one thing, trying to do welfare analysis in the standard Weberian way tends to obscure rather than to eliminate normative commitments (Putnam and Walsh 2007). Obscuring value judgments can be detrimental to the social scientist as policy adviser because it will hamper rather than promote trust in social science. For another, economists are in a prime position to contribute to ethical debates, for a variety of reasons, and should therefore take this responsibility seriously (Atkinson 2001).

The same demands calling for “mechanical objectivity” in the natural sciences and quantification in the social and policy sciences in the nineteenth century and mid-twentieth century are responsible for a recent movement in biomedical research, which, even more recently, have swept to contemporary social science and policy. Early proponents of so-called “evidence-based medicine” made their pursuit of a downplay of the “human element” in medicine plain:

Evidence-based medicine de-emphasizes intuition, unsystematic clinical experience, and pathophysiological rationale as sufficient grounds for clinical decision making and stresses the examination of evidence from clinical research. (Guyatt et al. 1992: 2420)

To call the new movement “evidence-based” is a misnomer strictly speaking, as intuition, clinical experience and pathophysiological rationale can certainly constitute evidence. But proponents of evidence-based practices have a much narrower concept of evidence in mind: analyses of the results of randomized controlled trials (RCTs). This movement is now very strong in biomedical research, development economics and a number of areas of social science, especially psychology, education and social policy, and especially in the English speaking world.

The goal is to replace subjective (biased, error-prone, idiosyncratic) judgments by mechanically objective methods. But, as in other areas, attempting to mechanize inquiry can lead to reduced accuracy and utility of the results.

Causal relations in the social and biomedical sciences hold on account of highly complex arrangements of factors and conditions. Whether for instance a substance is toxic depends on details of the metabolic system of the population ingesting it, and whether an educational policy is effective on the constellation of factors that affect the students’ learning progress. If an RCT was conducted successfully, the conclusion about the effectiveness of the treatment (or toxicity of a substance) under test is certain for the particular arrangement of factors and conditions of the trial (Cartwright 2007). But unlike the RCT itself, many of whose aspects can be (relatively) mechanically implemented, applying the result to a new setting (recommending a treatment to a patient, for instance) always involves subjective judgments of the kind proponents of evidence-based practices seek to avoid—such as judgments about the similarity of the test to the target or policy population.

On the other hand, RCTs can be regarded as “debiasing procedure” because they prevent researchers from allocating treatments to patients according to their personal interests, so that the healthiest (or smartest or…) subjects get the researcher’s favorite therapy. While unbalanced allocations can certainly happen by chance, randomization still provides some warrant that the allocation was not done on purpose with a view to promoting somebody’s interests. A priori , the experimental procedure is thus more impartial with respect to the interests at stake. It has thus been argued that RCTs in medicine, while no guarantor of the best outcomes, were adopted by the U.S. Food and Drugs Administration (FDA) to different degrees during the 1960s and 1970s in order to regain public trust in its decisions about treatments, which it had lost due to the thalidomide and other scandals (Teira and Reiss 2013; Teira 2010). It is important to notice, however, that randomization is at best effective with respect to one kind of bias, viz. selection bias. Important other epistemic concerns are not addressed by the procedure but should not be ignored (Worrall 2002).

In sections 2–5, we have encountered various concepts of scientific objectivity and their limitations. This prompts the question of how unified (or disunified) scientific objectivity is as a concept: Is there something substantive shared by all of these analyses? Or is objectivity, as Heather Douglas (2004) puts it, an “irreducibly complex” concept?

Douglas defends pluralism about scientific objectivity and distinguishes three areas of application of the concept: (1) interaction of humans with the world, (2) individual reasoning processes, (3) social processes in science. Within each area, there are various distinct senses which are again irreducible to each other and do not have a common core meaning. This does not mean that the senses are unrelated; they share a complex web of relationships and can also support each other—for example, eliminating values from reasoning may help to achieve procedural objectivity. For Douglas, reducing objectivity to a single core meaning would be a simplification without benefits; instead of a complex web of relations between different senses of objectivity we would obtain an impoverished concept out of touch with scientific practice. Similar arguments and pluralist accounts can be found in Megill (1994), Janack (2002) and Padovani et al. (2015)—see also Axtell (2016).

It has been argued, however, that pluralist approaches give up too quickly on the idea that the different senses of objectivity share one or several important common elements. As we have seen in section 4.1 and 5.1 , scientific objectivity and trust in science are closely connected. Scientific objectivity is desirable because to the extent that science is objective we have reasons trust scientists, their results and recommendations (cf. Fine 1998: 18). Thus, perhaps what is unifying among the difference senses of objectivity is that each sense describes a feature of scientific practice that is able to inspire trust in science.

Building on this idea, Inkeri Koskinen has recently argued that it is in fact not trust but reliance that we are after (Koskinen forthcoming). Trust is something that can be betrayed, but only individuals can betray whereas objectivity pertains to institutions, practices, results, etc. We call scientific institutions, practices, results, etc. objective to the extent that we have reasons to rely on them. The analysis does not stop here, however. There is a distinct view about objectivity that is behind Daston and Galison’s historical epistemology of the concept and has been defended by Ian Hacking: that objectivity is not a—positive—virtue but rather the absence of this or that vice (Hacking 2015: 26). Speaking of objectivity in imaging, for instance, Daston and Galison write that the goal is to

let the specimen appear without that distortion characteristic of the observer’s personal tastes, commitments, or ambitions. (Daston and Galison 2007: 121)

Koskinen picks up this idea of objectivity as absence of vice and argues that it is specifically the aversion of epistemic risks for which the term is reserved. Epistemic risks comprise “any risk of epistemic error that arises anywhere during knowledge practices’ (Biddle and Kukla 2017: 218) such as the risk of having mistaken beliefs, the risk of errors in reasoning and risks related to operationalization, concept formation, and model choice. Koskinen argues that only those epistemic risks that relate to failings of scientists as human beings are relevant to objectivity (Koskinen forthcoming: 13):

For instance, when the results of an experiment are incorrect because of malfunctioning equipment, we do not worry about objectivity—we just say that the results should not be taken into account. [...] So it is only when the epistemic risk is related to our own failings, and is hard to avert, that we start talking about objectivity. Illusions, subjectivity, idiosyncrasies, and collective biases are important epistemic risks arising from our imperfections as epistemic agents.

Koskinen understands her account as a response to Hacking’s (2015) criticism that we should stop talking about objectivity altogether. According to Hacking, “objectivity” is an “elevator” or second-level word, similar to “true” or “real”—“Instead of saying that the cat is on the mat, we move up one story and and say that it is true that the cat is on the mat” (2015: 20). He recommends to stick to ground-level questions and worry about whether specific sources of error have been controlled. (A similar elimination request with respect to the labels “objective” and “subjective” in statistical inference has been advanced by Gelman and Hennig (2017).) In focussing on averting specific epistemic risks, Koskinen’s account does precisely that. Koskinen argues that a unified account of objectivity as averting epistemic risks takes into account Hacking’s negative stance and explains at the same time important features of the concept—for example, why objectivity does not imply certainty and why it varies with context.

The strong point of this account is that none of the threats to a peculiar analysis puts scientific objectivity at risk. We can (and in fact, we do) rely on scientific practices that represent the world from a perspective and where non-epistemic values affect outcomes and decisions. What is left open by Koskinen’s account is the normative question of what a scientist who cares about her experiments and inferences being objective should actually do. That is, the philosophical ideas we have reviewed in this section stay mainly on the descriptive level and do not give an actual guideline for working scientists. Connecting the abstract philosophical analysis to day-to-day work in science remains an open problem.

So is scientific objectivity desirable? Is it attainable? That, as we have seen, depends crucially on how the term is understood. We have looked in detail at four different conceptions of scientific objectivity: faithfulness to facts, value-freedom, freedom from personal biases, and features of community practices. In each case, there are at least some reasons to believe that either science cannot deliver full objectivity in this sense, or that it would not be a good thing to try to do so, or both. Does this mean we should give up the idea of objectivity in science?

We have shown that it is hard to define scientific objectivity in terms of a view from nowhere, value freedom, or freedom from personal bias. It is a lot harder to say anything positive about the matter. Perhaps it is related to a thorough critical attitude concerning claims and findings, as Popper thought. Perhaps it is the fact that many voices are heard, equally respected and subjected to accepted standards, as Longino defends. Perhaps it is something else altogether, or a combination of several factors discussed in this article.

However, one should not (as yet) throw out the baby with the bathwater. Like those who defend a particular explication of scientific objectivity, the critics struggle to explain what makes science objective, trustworthy and special. For instance, our discussion of the value-free ideal (VFI) revealed that alternatives to the VFI are as least as problematic as the VFI itself, and that the VFI may, with all its inadequacies, still be a useful heuristic for fostering scientific integrity and objectivity. Similarly, although entirely “unbiased” scientific procedures may be impossible, there are many mechanisms scientists can adopt for protecting their reasoning against undesirable forms of bias, e.g., choosing an appropriate method of statistical inference, being transparent about different stages of the research process and avoiding certain questionable research practices.

Whatever it is, it should come as no surprise that finding a positive characterization of what makes science objective is hard. If we knew an answer, we would have done no less than solve the problem of induction (because we would know what procedures or forms of organization are responsible for the success of science). Work on this problem is an ongoing project, and so is the quest for understanding scientific objectivity.

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

• Norton, John, manuscript, The Material Theory of Induction , retrieved on 9 January 2020.
• Objectivity , entry by Dwayne H. Mulder in the Internet Encyclopedia of Philosophy .

Bayes’ Theorem | confirmation | feminist philosophy, interventions: epistemology and philosophy of science | feminist philosophy, interventions: philosophy of biology | Feyerabend, Paul | hermeneutics | incommensurability: of scientific theories | Kuhn, Thomas | logic: inductive | physics: experiment in | science: theory and observation in | scientific realism | statistics, philosophy of | underdetermination, of scientific theories | Weber, Max

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Essay on Science: Meaning, Scope, Nature, Technology and Society

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Essay on Science:- 1. Meaning and Definitions of Science 2. Scope of Science 3. Nature of Science 4. Physical Science 5. Science and Social Environment 6. Science and Technology 7. Science and Society 8. Scientific Method and Its Steps.

• Essay on Scientific Method and Its Steps

Essay # 1. Meaning and Definitions of Science :

Meaning of Science:

The English word Science is derived from a Latin Verb ‘Scire’, which means ‘to know’ and Latin Noun ‘Scientia’ which means ‘knowledge’. Meaning of Science is based on German word ‘ Wissenchaft’, which means systematic, organized knowledge. Thus, Science is a systematized knowledge.

The necessity and curiosity of man to know about himself and his surroundings has led him to investigate, find and to know about living beings and nature, which to verifiable knowledge of facts. But Science is not always about the collection of facts or development of new concepts or ideas. It is all about the passion for the discovery that drives one to explore the environment and the nature in every aspect.

Science is basically founded to investigate the nature and its processes. Although there are a number of other methods that can be utilized to acquire the knowledge about nature, but science is considered as the only one that results in the acquisition of reliable knowledge. Hence, Rene Descartes said, “Science is a method of investigating nature that discovers reliable knowledge about it.”

Science is the investigation of unknown phenomena and it also looks and compares with existing principles, theories and practices. Science is both a particular kind of activity and also the result of that activity. Science uses tools like observation, measurement and scientific experimentation and is entirely based on the observable facts.

Science is observation, identification, description, experimentation, investigation and theoretical explanation of the phenomenon that occur in nature.

Science could be described as the study, which attempts to perceive and understand the nature of the universe both living and non-living in its part and as a whole.

Definitions of Science :

During early times people perceived Science, as what the scientist does. There are many definitions available, though not a single definition could be universally accepted.

Some of the definitions are mentioned here to understand it from different angles:

1. According to Columbian Dictionary:

“Science is an accumulated and systematized learning in general usage restricted to natural phenomenon”.

2. Einstein (1879-1955):

“Science is an attempt to make the chaotic diversity of our sense experience corresponds to logically uniform system of thought”.

3. Fitzpatrick (1960):

“Science is a cumulative and endless series of empirical observations, which results in the formation of concepts and theories, with both concepts and theories being subject to modification in the light of further empirical observations. Science is both a body of knowledge and the process of acquiring it”.

4. Bronowski, J. (1956):

“Science as the organization of our knowledge in such a way that it commands or makes possible the explanation of more of the hidden potentialities found in the environment”.

5. Conant (1957):

“An interconnected series of concepts and conceptual schemes that have developed as a result of experimentation and observation and are fruitful of further experimentation and observation”.

6. Fisher (1975):

“Science is the body of Knowledge obtained by methods, based upon observation”.

The above definitions clearly reveal that Science is both a process and product. A comprehensive definition of Science would be “science is a systematized knowledge gained through human observation and experi­mentation of cause revealing the unknown phenomenon of nature and universe both living and non-living involving the process of critical, creative thinking and investigation including sometimes sudden insights too.”

Science = Process + Product

= Methods + Knowledge

= Scientific Method + Scientific Attitude + Scientific Knowledge

Essay #  2. Scope of Science :

Science is a body of knowledge obtained by methods based upon observation. Observation is authentic and that it is only through the senses of man that observations can be made. Thus, anything outside the limits of man’s senses is outside the limits of science. In other words, science deals with the universe and galaxies in the forms of matter and energy which is in the form of living and non-living.

Science employs a number of instruments to extend mail’s senses to the extremely minute to very vast, to the short-time duration or long-time duration, to dilute or to concentrate and so on and so forth which does not alter the conclusion that science is limited to that which is observable.

Thus, as in any other discipline contemporary experimental techniques set up some practical limitations but these are not to be confused with the intrinsic limitations inherent in the very nature of science. The knowledge of science is tested and retested and also reinvented.

Today the disciplines of Science and Social Sciences are drawing into each other. Behavioural zoologists study the sociology and psychology of animals. Archaeologists derive new insights from the rapid advances in chemical and physical analysis. Hence sciences should be understood with interdisciplinary approach within science as a whole. Biology draws on chemistry, physics and geology.

ADVERTISEMENTS: (adsbygoogle = window.adsbygoogle || []).push({}); Essay # 3. Nature of Science:

Human by birth has quest for knowledge as they are curious of knowing about nature. They have a highly developed brain because of which they can observe precisely, correlate observations and predict future happenings on the basis of their observation. This ability helped humans to adjust to nature. The process of observing, describing, exploring and using the physical world is science.

Science has certain characteristics which distinguish it from other spheres of human endeavour.

These characteristics define the nature of science as discussed below:

Science is a Particular way of Looking at Nature :

1. Science is a way of learning about what the nature is, how the nature behaves and how the nature got to be the way it is.

2. Science focuses exclusively on the nature.

3. It is not simply a collection of facts; rather it is a path to understand the phenomenon underlying.

(i) Science is, just the nature existing around you.

(ii) Every day we look at the rising sun and pay great respect to it for bestowing the earth with its light in energy form.

(iii) The knowledge of all that is in the universe from the tiniest sub­atomic particles in an atom to universe and galaxies.

Science as a Rapidly Expanding Body of Knowledge :

1. Science is the dynamic, ever expanding knowledge, covering every new domain of experiences.

2. Knowledge refers to the product of science, such as the concepts and explanations.

3. Research being carried out in the field of science resulted in developing more knowledge at a faster pace sometimes by replacing old concepts, ideas or principles.

The technological developments that took place in recent times enhanced the acceleration of knowledge.

Science as an Interdisciplinary Area of Learning:

1. In the last two decades there have been studies claiming that science is becoming even more an interdisciplinary area of learning.

2. Science cannot be taught in isolation. All the branches of science are interdependent upon all other and there are a number of facts and principles which are common to various science subjects.

3. Knowledge started expanding day by day; scientists started specialising in certain areas. Hence the knowledge has been organized for convenience into different disciplines.

Environmental science is an interdisciplinary academic field that integrates physics, biological and information sciences (including ecology, biology, physics, chemistry, zoology, mineralogy, oceanology, limnology, soil science, geology of atomospheric science and geodesy).

Science as a Truly International Enterprise :

1. International collaboration in most of the projects is the order of the day.

2. In collaborative research, visibility among the peer and active exploitation of complementary capabilities increase.

3. Share the costs of the projects that are large in scale and scope.

4. Able to access expensive physical resources.

5. Exchange ideas in order to encourage greater creativity.

The large Hadron collides; at the European Organization for Nuclear Research (CERN) has been build up by scientists drawn from many countries including India. The experiment on this machine is being conducted by scientists from many countries including many Indian scientists. In this sense, science do not belong to any single country or a group of countries and it would be morally and ethically wrong to deny the fruits of scientific development to any country in the world.

Science as Always Tentative :

Scientific models are always being questioned. Up-and-coming scientists always find gaps or errors in existing scientific models and develop a new one in place of them. In scientific field models have been tested and refined to such an extent that errors are likely to be minor. The real evidences need to be scrutinized carefully.

Marine researchers have expressed concern about the effect of global warming on the future of coral reefs because increasing sea temperature cause coral bleaching. Bleaching happens; the corals expel the algae that live within their cells die, when temperature rises. Recent research have tentatively showed that some algae may be able to adapt to temperature rises, consequently improved the chances that corals can survive.

Tentative Nature of Scientific Theories :

1. Scientific theories took decades in their development.

2. When two competing theories explain their observations related to a certain phenomenon, Scientists prefer to accept a theory which explains larger number of observations with few assumptions.

There was a time when both the geocentric and the heliocentric theories explained all the planetary observations. However geocentric theory had to introduce a new assumption every time. On the other hand, the heliocentric theory with just one assumption that all the planets revolve round the sun, it explained every available observation and eventually survived.

The fact remains that scientific theories are tentative and are always subject to change.

Science Promotes Skepticism :

“In science, keeping an open mind is a virtue just not so open that your brains fall out”-James Oberg.

1. Skepticism does not mean doubting the validity of everything, rather to judge the validity of a claim based on objective empirical evidences.

2. David Hume, the 18th century philosopher viewed that we should accept nothing as true unless the evidences available makes the non-existence of the thing more miraculous than its existence.

3. We examine the available evidences before reaching a decision until sufficient evidences are found.

Scientists are Highly Skeptic People :

‘Science is what scientists do’.

1. The scientists in different fields try to describe the phenomena in nature and establish their relationships.

2. After having described the phenomena, scientists attempt to find out the reason behind and make predictions.

3. Scientists use ideas of their own and of others as tools for testing and gaining knowledge. They use many resources to get valid answers to their questions and problems, by designing their own experiments and invent new tools with which they observe and check different phenomena. Hence, scientists are highly skeptic people.

For instances, if we look at Newton’s story the way he was inspired to formulate his theory of gravitation by watching the fall of an apple from a tree speaks his skeptic nature. Though many scientists and other common men were aware that all the objects descend perpendicularly to the ground, they never pondered upon it. This incident prompted Newton to explore the possibility of connecting gravity with the force that kept the moon on its orbit. This led him to the universal law of gravity.

Charles Goodyear (1800) a chemist and manufacturing engineer who developed vulcanized rubber. His discovery was accidental, where he explored the situation and after five years of searching for a more stable rubber and stumbling upon the effectiveness of heating.

Science Demands Perseverance from its Practitioners :

1. The important characteristic of science that brings development and progress is perseverance of scientists.

2. Scientists getting an inspirational idea or a creative thought have to persist with the idea to take it to its logical conclusions, based on facts or observations.

3. Scientists may work alone or join with others in developing the idea further to find out ways to discover or invention, While at other times the scientists can make only a beginning and then others join them in developing the idea further.

The discovery of the wonder drug pencillin by Alexander Fleming in 1929 is the result of an incident happened by a chance which led to serious observation followed by hard work paved the way for discovery of many other antibiotics like Streptomycin and Erythromycin.

Science as an Approach to Investigate and as a Process of Constructing Knowledge :

1. The investigations in science involve some form of scientific method.

2. Scientists for seeking solution to a problem use different methods like observation, prediction and sometimes experimentation to study the cause and effect relationship.

3. Whatever we observe through our senses (information) is sent to the brain and the brain processes the information by registering, classifying, generalising etc., and converts into knowledge. Sensory perception is primary in knowledge development.

4. Here, the individual constructs the knowledge on his own by applying their own mental abilities and intelligence to process the information received through senses.

5. The basic unit of knowledge is fact. In science any repeatedly verifiable observation becomes a fact.

6. Scientific approach always is based on cause and effect relation.

Examples of facts are:

i. Solids have definite shape and volume.

ii. The rainbow is seen in a direction opposite to that of the sun.

Essay #  4. Physical Science :

The child is interested to learn things which are related to his experiences. This could be possible only when the subjects are integrated and correlated rather than in isolation. The other physical sciences also have equally contributed a lot to the field of biological studies.

Obviously we can’t teach and understand each and every thing about a particular branch of science without the help of other sciences. The child on the other hand can’t appreciate and understand the branches of science in isolation from others. The study of interrelatedness helps the child to understand the concepts easily, more interesting and natural.

Science cannot be taught in isolation. All the branches of science are interdependent upon each other and there are a number of facts and principles which are common to various science subjects. This however does not mean that the teacher of one branch of science ought to know everything of other branches of science.

But it is very much essential that he should have sufficient knowledge of other sciences so as to bring about integration of subjects. He should also know where to depart from his own subject and how much should he venture into areas which are not his own.

The following example may be taken:

1. A teacher while teaching the sense organs says an eye should make a parallelism with a camera, which the student has learnt in physics. To understand the images, knowledge of image formation by the convex lens is essential.

E.g. (a) The rays which pass through the centre of the lens travel straight without any change in direction.

(b) The rays which run parallel to the principal axis pass through the focus of the lens after refraction from the lens.

Again when the teacher is teaching the same topic in the period of human physiology, the defects in the eye i.e., short sightedness (by the elongation of the eyeball and the image in formed a little in front of the retina and not exactly on the retina) he should know other factors also which cause the shifting of the image.

E.g. (a) By changing the distance between the lens and object.

(b) By changing the distance between the lens and the screen.

(c) By changing the total length of lens.

If the teacher possesses knowledge of physics he can most successfully correlate his topic with other branches of science and make the whole knowledge easily acceptable to the children.

2. Similarly while teaching digestive system the teacher should have adequate knowledge of chemistry without the help of which he cannot justify the topic.

The teacher must correlate it by telling about:

(a) Soluble and insoluble constituents of our diet.

(b) Chemistry of different digestive juices and their effect on the constituents of food that we take.

(c) The final products and the process of assimilation of products by the membranes of different organs. This will involve the reference of concepts of osmosis, density and the pressure etc.

Science is universal; it has no barrier of any kind as too has no barriers. The recent advances in the field of science and technology and its wide application as well as their use in daily life situation justify the utilitarian value of science. Taxonomy reveals the unity in diversity. Evolution and mutation theories help us understand the relation of living forms.

Motion, Mass and Energy related theories relate Universe, Sun, Earth and all other planets and their existence. Further their relation to life forms. Hence in nature everything is in relation and co-existence. This is what has to be understood by the student in the study of scientific theories and phenomenon.

Essay # 5. Science and Social Environment:

Relating science education with the environment of a child has been the prime concern of educationists. The environment of the child includes natural and social environment.

In science we learn about the nature’s phenomena. Human is a part of nature. Therefore, every effort should be made to integrate science with learning the environment. The science curriculum should address issues and concerns related to environment such as climate change, acid rain, growth of water, eutrophication and various types of pollutions etc. Further, it should be applied to society to understand social phenomenon in a scientific way and solve all social problems with all objectivity and universal application.

Science teachers should aim to enlighten the young minds with the wonders of science. They should be engaged to construct the knowledge through an interdisciplinary approach appreciating its relation and impact on the social and natural environment. They can recognize the competence of science by doing activities related to their everyday life.

Current issues and events in science like new technological innovations, scientific discoveries, can be examined through social, economic and ethical perspectives to help students in relating these issues with one another and explore their areas of interest.

The significance of chemistry to society can be highlighted by discussing the chemical components used in products that have altered agriculture, food, health, medicine, electronics, transportation, technology and the natural environments. To understand its relevance to home economics, one can think what happens to the electricity bill if solar cooker, solar heater, solar lanterns and CFL (compact fluorescent lamp) are used.

For Instances- Bhopal Tragedy Unforgettable Industrial Disaster :

Industries are the symbols of development, but other side of the coin is lack of safety measures and irresponsibility of emitting pollutants. On 2 nd December 1984 about 3000 human beings died and 5000 were effected seriously, thousands of cattle, birds, dogs, and cats died in just one night at Bhopal tragedy.

These mass deaths were due to the leakage of Methyle Isocyanate (MIC) into the air from an insecticide factory managed by union carbide. Thousands of lives helplessly crushed in this incident. This is unforgettable industrial disaster towards air pollution.

Essay # 6. Science and Technology :

Technology is often equated to applied sciences and its domain is generally thought to include mechanical, electrical, optical, electronic devices and instruments, the house hold and commercial gadgets, equipment used in physics, chemistry, biology, nuclear science etc. These various sub-domains of technology are interrelated. Modern technology is an applied science because the basic principles of sciences are applied to develop the technology.

Science and technology are linked to each other. Discoveries in science have paved the way for the evolution of new technologies. At the same time technology has been instrumental in the development of science.

Han’s Christian Oersted, one of the leading scientists of the 19 th century, played a crucial role in understanding electromagnetism. In 1820 he discovered that a compass needle got deflected when an electric current passed through a metallic wire placed nearby. Through this he showed that electricity and magnetism were related phenomena. His research later created technologies such as radio, television and fiber optics.

The development of microscope by Antony Van Leeuwenhock, where he interwined optical principles with astronomical and biological understanding which further led to the development of the telescope.

Thus, science influences technology by providing knowledge and methodology. But on the other hand technology also influences science by providing equipments to find out the unknown phenomenon of the nature. This shows interdependence of science and technology.

In science we inquire how a natural phenomenon occurs, while in technology we deal with how the scientific processes can also be used for human welfare. Technology as a discipline has its own autonomy and should not be regarded as a mere extension of science.

Basically science is an open ended exploration; its end results are not fixed in advance. Technology on the other hand, is also an exploration but usually with a definite goal in mind. Science is universal; technology is goal oriented and often local specific.

People today are faced with an increasingly fast-changing world where the most important skills are flexibility in adapting to new demands and creativity in taking advantages of new opportunities. These imperatives have to be kept in mind in shaping science education.

Essay # 7. Science and Society :

The applications of science and technology have led to the remarkable improvement in the quality of human life. It has given lot of comfort and leisure to the human kind on one side and equipped it with skills needed for problem solving and decision making on the other side. It has changed the outlook of the individual on different beliefs, myths, taboos and superstitions.

People started working with logical thinking, objectivity and open mindedness. Modern society believed in the co-existence of diversity in social and political thinking. Science always works for the welfare of our future generations by talking about sustainable development. Society is also showing its concern using the scientific knowledge for peace and prosperity of the society.

For instances, consuming tobacco (Gutkha, cigarettes, beedi, khaini) damages the internal organs of the body. The numbers of addicted people at the age of 15 or below are 57.57 lakhs (68%) both in Telengana and Andhra. When they reach 30 yrs. of age thin internal organs becomes damaged, this may lead to several problems and sometimes lead to death.

It is a dangerous trend in our country. So, we have to inculcate healthy habits in children by teaching science. Many youth are also addicted to alcohol which damages the liver and other body organs which in turn also affects human resource development.

Let Us Think It Over:

Do you know that our eyes can live even after our death? By donating our eyes after we die, we can give sight to a blind person.

About 35 million people in the developing world are blind and most of them can be cured. About 4.5 million people are with corneal blindness, can be cured by corneal transplantation of donated eyes. Out of these 4.5 million, 60% are children below the age of 12 yrs. So, if we got the gift of vision, let us pass it on to somebody who does not have it.

Essay # 8. Scientific Method and Its Steps:

1. The development of scientific attitude and training in scientific method are two cardinal aims for the teaching of science. In other words it is a method of solving a problem scientifically.

2. Scientific method involves reflective thinking, reasoning and results from the achievement of certain abilities, skills and attitudes.

Definition of Scientific Method :

Carl Pearson says, ‘The scientific method is marked by the following features:

1. Careful and accurate classification of facts.

2. Observation of their co-relation and sequence.

3. Discovery of scientific law by creative imagination, and self-criticism.

4. The final touch-stone of equal validity for all normally constituted needs.

Steps of Scientific Method:

Observation :

Observation is the base for science. It knows the phenomenon through senses. Without control of external or internal situations.

1. It is the way we perceive the nature and using the senses and processed through the faculty of brain.

2. It is a process of checking conclusions. After observation we try to explain what we have seen based on cause and effect relation. In science repeatedly verifiable observations becomes a fact.

Facts are specific verifiable information obtained through observation and measurement. They are verifiable with reference to time and place.

Some facts do not require the time and place to be mentioned. Ex- Iron is a greyish hard metal.

Some facts are specific like ‘water boils at 100°C at 760mm Hg of pressure.

A concept is an idea or a mental image of an object is generalised forms of specific relevant direct experiences interpreted in a language or word form for communication.

1. Concepts. Ex. plant, animal etc.

2. According to Bruner, every concept has five elements i.e. name, example (positive & negative), attributes (characteristics) attribute value and rule (definition).

3. Concepts formed without direct experiences may lead to misconceptions. Hence, care should be taken in provide direct experiences in learning process.

Principles :

Principles are based on several concepts. They are the representation of phenomena on which the activities or behaviour can be generalised to some extent.

A number of concepts combine in a way to convey meaning which can be tested and verified universally, becomes a principle.

Ex- Mytosis, Meiosis, Glycolysis, Photosynthe sis, Mutations, Evolution etc.

Scientific Inquiry :

It occupies a prominent place in science as it helps pupils to understand how scientific ideas are developed.

1. It is broadly defined as a search for truth or knowledge. Emphasis is placed on the aspects of search rather than on the mere acquisition of knowledge.

2. Empirical testing, reasoning and controlled experimenting are some of the methods of science inquiry.

The steps in scientific methods are illustrated with a specific example:

The teacher demonstrates an experiment to the students to show that water boils at low temperature under low pressure.

1. Sensing the Problem:

The teacher provides a situation in which the students feel the need of asking some questions. Teacher may also put questions which require reflective thinking and reasoning on the part of the students, this may become a problem to solve. The interest of the students, availability of the material and its utility should be considered.

A flask was taken and filled it half with water. Boil the water over a flame. Remove the flame. Cork the flask. Invert it and pour cold water on the flask. The students observe the process carefully and saw that water has begun to boil again when cold water is poured on the bottom of the inverted flask. They at once sense a problem for themselves finding out the reason and explanation of what they have seen.

2. Defining the Problem:

The student now defines the problem in a concise, definite and clear language. There should be some key-words in the statement of the problem, which may help in better understanding the problem.

The student can give different statements such as:

(i) Why is water boiling?

(ii) Why did the water boil first?

(iii) Why was the flask corked and then inverted?

(iv) Why was cold water poured over the bottom of the inverted flask?

(v) Why did the water boil in the flask when cold water is poured over the inverted flask?

Of all these statements, the last one is in fact the problem which should be solved.

3. Analysis of the Problem:

The student now fined the key words and phrases in the problem which provide clue to further study of the problem. At the same time, the students must have knowledge of every key word and the understanding of the whole problem. In our selected problem ‘water boil’ or the boiling of the water are the key words which gives us clue to find information regarding the boiling of water under different conditions.

Collection of Data :

After analysis of the problem the teacher suggests references on the problem. The student needs to plan the subsequent activities. They have to discuss, consult references, use audio-visual aids such as models, pictures, specimens, organise field trips and do the experimentation carefully. Unnecessary data should also be discarded.

Formulation of Tentative Solutions or Hypothesis :

After collection of data, the students are asked to formulate some tentative hypothesis. A hypothesis is the probable solution to the problem in hand, which should be free from bias and self-inclination.

The students can suggest the hypothesis like:

Water will also boil:

(i) When flask is not inverted.

(ii) When water is not boiled but only warmed.

(iii) When hot water is poured over the inverted flask containing cold water.

(iv) When hot water is poured over the inverted flask containing boiled water.

(v) When cold water is poured over the flask containing cold water.

(vi) When cold water is poured over the inverted flask containing boiled water.

These are some of the hypothesis the students can suggest.

Selecting and Testing the Most Appropriate Hypothesis :

The students can select the most tenable hypothesis by rejecting others through experimentation and discussion.

The students have found out that water begins to boil again in an inverted flask when cold water is poured over it. In no other condition this was possible and so all other hypothesis were rejected.

Drawing Conclusions and Making Generalisations :

In this step, conclusions are drawn from the experiments. The results should support the expected solution. Experiments can be repeated to verify the consistency and correctness of the conclusion drawn and should be properly reported. When some conclusions are drawn from different sets of experimentation under similar situations, they may go for generalisation of their conclusion.

The generalisation can be made by arranging a set of experiments which also show the same conclusion already reached at.

The effect of varying pressure on boiling point of water can be found out by conducting experiments. From these conditions, one can generalise that pressure has a direct effect on the boiling point of water i.e. the increase in pressure raises the boiling point of water and vice-versa.

Application of Generalization to New Situations :

The student should apply generalization under new situations in his daily life minimising the gap between classroom situation and real life situation.

The student will apply the generalization that increase in pressure increases the boiling point of water and vice-versa, to explain the reason of – ‘why’ is it difficult to cook meat and pulses at higher altitudes.

Why do the pulses take lesser time for cooking in pressure cooker.

In this way the student will apply the generalization to other life situations.

Scientific Method- A Critical View :

A few points about the scientific method need to be emphasized.

Scientific method is not a prescribed pathing for making discoveries in science. Very rarely the method has remained a key to discovery in science. It is the attitude of inquiry, investigation and experimentation rather than following set steps of a particular method that leads to discoveries and advancement in science.

Sometimes a theory may suggest a new experiment at other times an experiment may suggest a new theoretical model. Scientists do not always go through all the steps of the method and not necessarily in the order we have outlines above. Investigation in science often involves repeated action on any one or all steps of the scientific method in any order.

Many important and path breaking discoveries in science have been made by trial and error, experimentation and accidental observation. The Rontgen and Fleming both of them did not set out the following scientific steps to discover X-rays and penicillin, but they had qualities of healthy intuition and perseverance which took them to their goals. Besides intuition informed guesswork, creativity, an eye for an unusual occurrence, all played a significant role in developing new theories, and there by progress in science.

The validity of a hypothesis depends solely on the experimental test and not on any other attributes. There is no authority in science that tells you what you can criticize and what you cannot criticize. Thus, science is highly objective discipline.

A scientific method with its linear steps makes us feel that science is a ‘closed box approach’ of thinking. However in practice science is more about thinking ‘out of the box’. There is tremendous scope for creativity in science. Many times in science an idea or a solution to a vexing problem appears to arise out of creativity and imagination. Ex- The stories of Archimedes, Newton, Robert Hook, Fleming and Madam Curie etc.

People keep floating all kinds of theories; often they narrow their arguments in scientific terms. This may create lot of confusion among them, but we should remember that a theory is valid only if it passes the test of experimentation, otherwise it may just be a matter of faith.

The scientific method imposes operational limitation on science. It does not help us to make aesthetic or value judgment. For example, frequency of the colour of paintings may be determined but there is no scientific method to label the paintings of two artists as great or not so great. Scientific method does not prove or refute the ideas such as existence of God and existence of life after death.

Following scientific method does not ensure that a discovery can be made. However, the skills learnt in making observation, analysis, hypothesis, prediction from a hypothesis and it’s testing by experimentation help us in developing scientific attitude.

All of us will benefit immensely if we imbibe the spirit of scientific method in our personal lives. The scientific method tells us to be honest in reporting our observations or experimental results, keep an open mind and to be ready to accept other points of view. If our own view is proved wrong.

Scientific method is a logical approach to problem-solving.

Related Articles:

• Essay on Environmental Science
• Content-cum-Methodology in Teaching: Concept, Nature, Need and Steps

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