in scientific research no hypothesis can be conclusively proven true

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1.5: Scientific Investigations

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• Suzanne Wakim & Mandeep Grewal
• Butte College

What Turned the Water Orange?

If you were walking in the woods and saw this stream, you probably would wonder what made the water turn orange. Is the water orange because of something growing in it? Is it polluted with some kind of chemicals? To answer these questions, you might do a little research. For example, you might ask local people if they know why the water is orange, or you might try to learn more about it online. If you still haven't found answers, you could undertake a scientific investigation. In short, you could "do" science.

"Doing" Science

Science is more about doing than knowing. Scientists are always trying to learn more and gain a better understanding of the natural world. There are basic methods of gaining knowledge that is common to all of science. At the heart of science is the scientific investigation. A scientific investigation is a plan for asking questions and testing possible answers in order to advance scientific knowledge.

Figure \(\PageIndex{2}\) outlines the steps of the scientific method. Science textbooks often present this simple, linear "recipe" for a scientific investigation. This is an oversimplification of how science is actually done, but it does highlight the basic plan and purpose of any scientific investigation: testing ideas with evidence. We will use this flowchart to help explain the overall format for scientific inquiry.

Science is actually a complex endeavor that cannot be reduced to a single, linear sequence of steps, like the instructions on a package of cake mix. Real science is nonlinear, iterative (repetitive), creative, unpredictable, and exciting. Scientists often undertake the steps of an investigation in a different sequence, or they repeat the same steps many times as they gain more information and develop new ideas. Scientific investigations often raise new questions as old ones are answered. Successive investigations may address the same questions but at ever-deeper levels. Alternatively, an investigation might lead to an unexpected observation that sparks a new question and takes the research in a completely different direction.

Knowing how scientists "do" science can help you in your everyday life, even if you aren't a scientist. Some steps of the scientific process — such as asking questions and evaluating evidence — can be applied to answering real-life questions and solving practical problems.

Making Observations

A scientific investigation typically begins with observations. An observation is anything that is detected through human senses or with instruments and measuring devices that enhance human senses. We usually think of observations as things we see with our eyes, but we can also make observations with our sense of touch, smell, taste, or hearing. In addition, we can extend and improve our own senses with instruments such as thermometers and microscopes. Other instruments can be used to sense things that human senses cannot detect at all, such as ultraviolet light or radio waves.

Sometimes chance observations lead to important scientific discoveries. One such observation was made by the Scottish biologist Alexander Fleming (Figure \(\PageIndex{3}\)) in the 1920s. Fleming's name may sound familiar to you because he is famous for the discovery in question. Fleming had been growing a certain type of bacteria on glass plates in his lab when he noticed that one of the plates had been contaminated with mold. On closer examination, Fleming observed that the area around the mold was free of bacteria.

Asking Questions

Observations often lead to interesting questions. This is especially true if the observer is thinking like a scientist. Having scientific training and knowledge is also useful. Relevant background knowledge and logical thinking help make sense of observations so the observer can form particularly salient questions. Fleming, for example, wondered whether the mold — or some substance it produced — had killed bacteria on the plate. Fortunately for us, Fleming didn't just throw out the mold-contaminated plate. Instead, he investigated his question and in so doing, discovered the antibiotic penicillin.

Hypothesis Formation

To find the answer to a question, the next step in a scientific investigation typically is to form a hypothesis. A hypothesis is a possible answer to a scientific question. But it isn’t just any answer. A hypothesis must be based on scientific knowledge. In other words, it shouldn't be at odds with what is already known about the natural world. A hypothesis also must be logical, and it is beneficial if the hypothesis is relatively simple. In addition, to be useful in science, a hypothesis must be testable and falsifiable. In other words, it must be possible to subject the hypothesis to a test that generates evidence for or against it, and it must be possible to make observations that would disprove the hypothesis if it really is false.

A hypothesis is often expressed in the form of prediction: If the hypothesis is true, then B will happen to the dependent variable . Fleming's hypothesis might have been: "If a certain type of mold is introduced to a particular kind of bacteria growing on a plate, the bacteria will die." Is this a good and useful hypothesis? The hypothesis is logical and based directly on observations. The hypothesis is also simple, involving just one type each of mold and bacteria growing on a glass plate. This makes it easy to test. In addition, the hypothesis is falsifiable. If bacteria were to grow in the presence of the mold, it would disprove the hypothesis if it really is false.

Hypothesis Testing

Hypothesis testing is at the heart of a scientific investigation. How would Fleming test his hypothesis? He would gather relevant data as evidence. Evidence is any type of data that may be used to test a hypothesis. Data (singular, datum) are essentially just observations. The observations may be measurements in an experiment or just something the researcher notices. Testing a hypothesis then involves using the data to answer two basic questions:

• If my hypothesis is true, what would I expect to observe?
• Does what I actually observe match what predicted?

A hypothesis is supported if the actual observations (data) match the expected observations. A hypothesis is refuted if the actual observations differ from the expected observations.

Testing Fleming's Hypothesis

To test his hypothesis that the mold kills bacteria, Fleming grew colonies of bacteria on several glass plates and introduced mold to just some of the plates. He subjected all of the plates to the same conditions except for the introduction of mold. Any differences in the growth of bacteria on the two groups of plates could then be reasonably attributed to the presence/absence of mold. Fleming's data might have included actual measurements of bacterial colony size, like the data shown in the data table below, or they might have been just an indication of the presence or absence of bacteria growing near the mold. Data like the former, which can be expressed numerically, are called quantitative data. Data like the latter, which can only be expressed in words, such as present or absent, are called qualitative data.

Analyzing and Interpreting Data

The data scientists gather in their investigations are raw data. These are the actual measurements or other observations that are made in an investigation, like the measurements of bacterial growth shown in the data table above. Raw data usually must be analyzed and interpreted before they become evidence to test a hypothesis. To make sense of raw data and decide whether they support a hypothesis, scientists generally use statistics.

There are two basic types of statistics: descriptive statistics and inferential statistics. Both types are important in scientific investigations.

• Descriptive statistics describe and summarize the data. They include values such as the mean, or average, value in the data. Another basic descriptive statistic is the standard deviation, which gives an idea of the spread of data values around the mean value. Descriptive statistics make it easier to use and discuss the data and also to spot trends or patterns in the data.
• Inferential statistics help interpret data to test hypotheses. They determine how likely it is that the actual results obtained in an investigation occurred just by chance rather than for the reason posited by the hypothesis. For example, if inferential statistics show that the results of an investigation would happen by chance only 5 percent of the time, then the hypothesis has a 95 percent chance of being correctly supported by the results. An example of a statistical hypothesis test is a t-test. It can be used to compare the mean value of the actual data with the expected value predicted by the hypothesis. Alternatively, a t-test can be used to compare the mean value of one group of data with the mean value of another group to determine whether the mean values are significantly different or just different by chance.

Assume that Fleming obtained the raw data shown in the data table above. We could use a descriptive statistic such as the mean area of bacterial growth to describe the raw data. Based on these data, the mean area of bacterial growth for plates with mold is 56 mm 2 , and the mean area for plates without mold is 69 mm 2 . Is this difference in bacterial growth significant? In other words, does it provide convincing evidence that bacteria are killed by the mold or something produced by the mold? Or could the difference in mean values between the two groups of plates be due to chance alone? What is the likelihood that this outcome could have occurred even if mold or one of its products does not kill bacteria? A t-test could be done to answer this question. The p-value for the t-test analysis of the data above is less than 0.05. This means that one can say with 95% confidence that the means of the above data are statistically different.

Drawing Conclusions

A statistical analysis of Fleming's evidence showed that it did indeed support his hypothesis. Does this mean that the hypothesis is true? No, not necessarily. That's because a hypothesis can never be proven conclusively to be true. Scientists can never examine all of the possible evidence, and someday evidence might be found that disproves the hypothesis. In addition, other hypotheses, as yet unformed, may be supported by the same evidence. For example, in Fleming's investigation, something else introduced onto the plates with the mold might have been responsible for the death of the bacteria. Although a hypothesis cannot be proven true without a shadow of a doubt, the more evidence that supports a hypothesis, the more likely the hypothesis is to be correct. Similarly, the better the match between actual observations and expected observations, the more likely a hypothesis is to be true.

Many times, competing hypotheses are supported by evidence. When that occurs, how do scientists conclude which hypothesis is better? There are several criteria that may be used to judge competing hypotheses. For example, scientists are more likely to accept a hypothesis that:

• explains a wider variety of observations.
• explains observations that were previously unexplained.
• generates more expectations and is thus more testable.
• is more consistent with well-established theories.
• is more parsimonious, that is, is a simpler and less convoluted explanation.

Correlation-Causation Fallacy

Many statistical tests used in scientific research calculate correlations between variables. Correlation refers to how closely related two data sets are, which may be a useful starting point for further investigation. However, correlation is also one of the most misused types of evidence, primarily because of the logical fallacy that correlation implies causation. In reality, just because two variables are correlated does not necessarily mean that either variable causes the other.

A simple example can be used to demonstrate the correlation-causation fallacy. Assume a study found that both ice cream sales and burglaries are correlated; that is, rates of both events increase together. If correlation really did imply causation, then you could conclude that ice cream sales cause burglaries or vice versa. It is more likely, however, that a third variable, such as the weather, influences rates of both ice cream sales and burglaries. Both might increase when the weather is sunny.

An actual example of the correlation-causation fallacy occurred during the latter half of the 20th century. Numerous studies showed that women taking hormone replacement therapy (HRT) to treat menopausal symptoms also had a lower-than-average incidence of coronary heart disease (CHD). This correlation was misinterpreted as evidence that HRT protects women against CHD. Subsequent studies that controlled other factors related to CHD disproved this presumed causal connection. The studies found that women taking HRT were more likely to come from higher socio-economic groups, with better-than-average diets and exercise regimens. Rather than HRT causing lower CHD incidence, these studies concluded that HRT and lower CHD were both effects of higher socioeconomic status and related lifestyle factors.

Communicating Results

The last step in a scientific investigation is communicating the results to other scientists. This is a very important step because it allows other scientists to try to repeat the investigation and see if they can produce the same results. If other researchers get the same results, it adds support to the hypothesis. If they get different results, it may disprove the hypothesis. When scientists communicate their results, they should describe their methods and point out any possible problems with the investigation. This allows other researchers to identify any flaws in the method or think of ways to avoid possible problems in future studies.

Repeating a scientific investigation and reproducing the same results is called replication . It is a cornerstone of scientific research. Replication is not required for every investigation in science, but it is highly recommended for those that produce surprising or particularly consequential results. In some scientific fields, scientists routinely try to replicate their own investigations to ensure the reproducibility of the results before they communicate them.

Scientists may communicate their results in a variety of ways. The most rigorous way is to write up the investigation and results in the form of an article and submit it to a peer-reviewed scientific journal for publication. The editor of the journal provides copies of the article to several other scientists who work in the same field. These are the peers in the peer-review process. The reviewers study the article and tell the editor whether they think it should be published, based on the validity of the methods and significance of the study. The article may be rejected outright, or it may be accepted, either as is or with revisions. Only articles that meet high scientific standards are ultimately published.

• Outline the steps of a typical scientific investigation.
• What is a scientific hypothesis? What characteristics must a hypothesis have to be useful in science?
• Explain how you could do a scientific investigation to answer this question: Which of the following surfaces in my home has the most bacteria: the house phone, TV remote, bathroom sink faucet, or outside door handle? Form a hypothesis and state what results would support it and what results would refute it.
• Look at the areas of bacterial growth for the plates in just one group – either with mold (plates 1-5) or without mold (plates 6-10). Is there a variation within the group? What do you think could be possible sources of variation within the group?
• Compare the area of bacterial growth for plate 1 vs. plate 7. Does this appear to be more of a difference between the mold group vs. the no mold group than if you compared plate 5 vs. plate 6? Using these differences among the individual data points, explain why it is important to find the mean of each group when analyzing the data.
• Why do you think it would be important for other researchers to try to replicate the findings in this study?
• Is the energy level of the mice treated with the drug a qualitative or quantitative observation?
• At the end of the study, the scientist measures the size of the tumors. Is this qualitative or quantitative data?
• Would the size of each tumor be considered raw data or descriptive statistics?
• The scientist determines the average decrease in tumor size for the drug-treated group. Is this raw data, descriptive statistics, or inferential statistics?
• The average decrease in tumor size in the drug-treated group is larger than the average decrease in the untreated group. Can the scientist assume that the drug shrinks tumors? If not, what do they need to do next?
• Do you think results published in a peer-reviewed scientific journal are more or less likely to be scientifically valid than those in a self-published article or book? Why or why not
• Explain why real science is usually “nonlinear”?

Explore More

Watch this TED talk for a lively discussion of why the standard scientific method is an inadequate model of how science is really done.

Attributions

• Rio Tinto River by Carol Stoker, NASA, public domain via Wikimedia Commons
• Scientific Method by OpenStax, licensed CC BY 4.0
• Alexander Flemming by Ministry of Information Photo Division Photographer, public domain via Wikimedia Commons
• Text adapted from Human Biology by CK-12 licensed CC BY-NC 3.0

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Expert Commentary

Don’t say ‘prove’: How to report on the conclusiveness of research findings

This tip sheet explains why it's rarely accurate for news stories to report that a new study proves anything — even when a press release says it does.

Republish this article

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License .

by Denise-Marie Ordway, The Journalist's Resource February 13, 2023

This <a target="_blank" href="https://journalistsresource.org/media/dont-say-prove-research-tip-sheet/">article</a> first appeared on <a target="_blank" href="https://journalistsresource.org">The Journalist's Resource</a> and is republished here under a Creative Commons license.<img src="https://journalistsresource.org/wp-content/uploads/2020/11/cropped-jr-favicon-150x150.png" style="width:1em;height:1em;margin-left:10px;">

When news outlets report that new research studies prove something, they’re almost certainly wrong.

Studies conducted in fields outside of mathematics do not “prove” anything. They find evidence — sometimes, extraordinarily strong evidence.

It’s important journalists understand that science is an ongoing process of collecting and interrogating evidence, with each new discovery building on or raising questions about earlier discoveries. A single research study usually represents one small step toward fully understanding an issue or problem.

Even when scientists have lots of very strong evidence, they rarely claim to have found proof because proof is absolute. To prove something means there is no chance another explanation exists.

“Even a modest familiarity with the history of science offers many examples of matters that scientists thought they had resolved, only to discover that they needed to be reconsidered,” Naomi Oreskes , a professor of the history of science at Harvard University, writes in a July 2021 essay in Scientific American. “Some familiar examples are Earth as the center of the universe, the absolute nature of time and space, the stability of continents, and the cause of infectious disease.”

Oreskes points out in her 2004 paper “ Science and Public Policy: What’s Proof Got To Do With It? ” that “proof — at least in an absolute sense — is a theoretical ideal, available in geometry class but not in real life.”

Math scholars routinely rely on logic to try to prove something beyond any doubt. What sets mathematicians apart from other scientists is their use of mathematical proofs, a step-by-step argument written using words, symbols and diagrams to convince another mathematician that a given statement is true, explains Steven G. Krantz , a professor of mathematics and statistics at Washington University in St. Louis.

“It is proof that is our device for establishing the absolute and irrevocable truth of statements in our subject,” he writes in “ The History and Concept of Mathematical Proof .” “This is the reason that we can depend on mathematics that was done by Euclid 2300 years ago as readily as we believe in the mathematics that is done today. No other discipline can make such an assertion.”

If you’re still unsure how to describe the conclusiveness of research findings, keep reading. These four tips will help you get it right.

1. Avoid reporting that a research study or group of studies “proves” something — even if a press release says so.

Press releases announcing new research often exaggerate or minimize findings, academic studies have found . Some mistakenly state researchers have proven something they haven’t.

The KSJ Science Editing Handbook urges journalists to read press releases carefully. The handbook, a project of the Knight Science Journalism Fellowship at MIT , features guidance and insights from some of the world’s most talented science writers and editors.

“Press releases that are unaccompanied by journal publications rarely offer any data and, by definition, offer a biased view of the findings’ value,” according to the handbook, which also warns journalists to “never presume that everything in them is accurate or complete.”

Any claim that researchers in any field outside mathematics have proven something should raise a red flag for journalists, says Barbara Gastel , a professor of integrative biosciences, humanities in medicine, and biotechnology at Texas A&M University.

She says journalists need to evaluate the research themselves.

“Read the full paper,” says Gastel, who’s also director of Texas A&M University’s master’s degree program in science and technology journalism . “Don’t go only on the news release. Don’t go only on the abstract to get a full sense of how strong the evidence is. Read the full paper and be ready to ask some questions — sometimes, hard questions — of the researchers.”

2. Use language that correctly conveys the strength of the evidence that a research study or group of studies provides.

Researchers investigate an issue or problem to better understand it and build on what earlier research has found. While studies usually unearth new information, it’s seldom enough to reach definitive conclusions.

When reporting on a study or group of studies, journalists should choose words that accurately convey the level of confidence researchers have in the findings, says Glenn Branch , deputy director of the nonprofit National Center for Science Education , which studies how public schools, museums and other organizations communicate about science.

For example, don’t say a study “establishes” certain facts or “settles” a longstanding question when it simply “suggests” something is true or “offers clues” about some aspect of the subject being examined.

Branch urges journalists to pay close attention to the language researchers use in academic articles. Scientists typically express themselves in degrees of confidence, he notes. He suggests journalists check out the guidance on communicating levels of certainty across disciplines offered by the Intergovernmental Panel on Climate Change , created by the United Nations and World Meteorological Organization to help governments understand, adapt to and mitigate the impacts of climate change.

“The IPCC guidance is probably the most well-developed system for consistently reporting the degree of confidence in scientific results, so it, or something like it, may start to become the gold standard,” Branch wrote via email.

Gastel says it is important journalists know that even though research in fields outside mathematics do not prove anything, a group of studies, together, can provide evidence so strong it gets close to proof.

It can provide “overwhelming evidence, particularly if there are multiple well-designed studies that point in the same direction,” she says.

To convey very high levels of confidence, journalists can use phrases such as “researchers are all but certain” and “researchers have as much confidence as possible in this area of inquiry.”

Another way to gauge levels of certainty: Find out whether scholars have reached a scientific consensus ,  or a collective position based on their interpretation of the evidence.

Independent scientific organizations such as the National Academy of Sciences, American Association for the Advancement of Science and American Medical Association issue consensus statements on various topics, typically to communicate either scientific consensus or the collective opinion of a convened panel of subject experts.

3. When reporting on a single study, explain what it contributes to the body of knowledge on that given topic and whether the evidence, as a whole, leans in a certain direction. 

Many people are unfamiliar with the scientific process, so they need journalists’ help understanding how a single research study fits into the larger landscape of scholarship on an issue or problem. Tell audiences what, if anything, researchers can say about the issue or problem with a high level of certainty after considering all the evidence, together.

A great resource for journalists trying to put a study into context: editorials published in academic journals. Some journals, including the New England Journal of Medicine and JAMA , the journal of the American Medical Association, sometimes publish an editorial about a new paper along with the paper, Gastel notes.

Editorials, typically written by one or more scholars who were not involved in the study but have deep expertise in the field, can help journalists gauge the importance of a paper and its contributions.

“I find that is really handy,” Gastel adds.

4. Review headlines closely before they are published. And read our tip sheet on avoiding mistakes in headlines about health and medical research.

Editors, especially those who are not familiar with the process of scientific inquiry, can easily make mistakes when writing or changing headlines about research. And a bad headline can derail a reporter’s best efforts to cover research accurately.

To prevent errors, Gastel recommends reporters submit suggested headlines with their stories. She also recommends they review their story’s headline right before it is published.

Another good idea: Editors, including copy editors, could make a habit of consulting with reporters on news headlines about research, science and other technical topics. Together, they can choose the most accurate language and decide whether to ever use the word ‘prove.’

Gastel and Branch agree that editors would benefit from science journalism training, particularly as it relates to reporting on health and medicine. Headlines making erroneous claims about the effectiveness of certain drugs and treatments can harm the public. So can headlines claiming researchers have “proven” what causes or prevents health conditions such as cancer, dementia and schizophrenia.

Our tip sheet on headline writing addresses this and other issues.

“’Prove’ is a short, snappy word, so it works in a headline — but it’s usually wrong,” says Branch. “Headline writers need to be as aware of this as the journalists are.”

About The Author

Denise-Marie Ordway

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Biology library

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

• Controlled experiments
• The scientific method and experimental design

Introduction

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

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

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

5. Test the predictions.

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

Logical possibility

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

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

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Understanding Science

How science REALLY works...

• Understanding Science 101
• Misconceptions
• Testing ideas with evidence from the natural world is at the core of science.
• Scientific testing involves figuring out what we would  expect  to observe if an idea were correct and comparing that expectation to what we  actually  observe.
• Scientific arguments are built from an idea and the evidence relevant to that idea.
• Scientific arguments can be built in any order. Sometimes a scientific idea precedes any evidence relevant to it, and other times the evidence helps inspire the idea.

Misconception:  Science proves ideas.

Misconception:  Science can only disprove ideas.

Correction:  Science neither proves nor disproves. It accepts or rejects ideas based on supporting and refuting evidence, but may revise those conclusions if warranted by new evidence or perspectives.  Read more about it.

The core of science: Relating evidence and ideas

In this case, the term  argument  refers not to a disagreement between two people, but to an evidence-based line of reasoning — so scientific arguments are more like the closing argument in a court case (a logical description of what we think and why we think it) than they are like the fights you may have had with siblings. Scientific arguments involve three components: the idea (a  hypothesis  or theory), the  expectations  generated by that idea (frequently called predictions), and the actual observations relevant to those expectations (the evidence). These components are always related in the same logical way:

• What would we expect to see if this idea were true (i.e., what is our expected observation)?
• What do we actually observe?
• Do our expectations match our observations?

PREDICTIONS OR EXPECTATIONS?

When scientists describe their arguments, they frequently talk about their expectations in terms of what a hypothesis or theory predicts: “If it were the case that smoking causes lung cancer, then we’d  predict  that countries with higher rates of smoking would have higher rates of lung cancer.” At first, it might seem confusing to talk about a prediction that doesn’t deal with the future, but that refers to something going on right now or that may have already happened. In fact, this is just another way of discussing the expectations that the hypothesis or theory generates. So when a scientist talks about the  predicted  rates of lung cancer, he or she really means something like “the rates that we’d expect to see if our hypothesis were correct.”

If the idea generates expectations that hold true (are actually observed), then the idea is more likely to be accurate. If the idea generates expectations that don’t hold true (are not observed), then we are less likely to  accept  the idea. For example, consider the idea that cells are the building blocks of life. If that idea were true, we’d expect to see cells in all kinds of living tissues observed under a microscope — that’s our expected observation. In fact, we do observe this (our actual observation), so evidence supports the idea that living things are built from cells.

Though the structure of this argument is consistent (hypothesis, then expectation, then actual observation), its pieces may be assembled in different orders. For example, the first observations of cells were made in the 1600s, but cell theory was not postulated until 200 years later — so in this case, the evidence actually helped inspire the idea. Whether the idea comes first or the evidence comes first, the logic relating them remains the same.

Here, we’ll explore scientific arguments and how to build them. You can investigate:

Putting the pieces together: The hard work of building arguments

• Predicting the past
• Arguments with legs to stand on

Or just click the  Next  button to dive right in!

• Take a sidetrip
• Teaching resources

Scientific arguments rely on testable ideas. To learn what makes an idea testable, review our  Science Checklist .

• Forming hypotheses — scientific explanations — can be difficult for students. It is often easier for students to generate an expectation (what they think will happen or what they expect to observe) based on prior experience than to formulate a potential explanation for that phenomena. You can help students go beyond expectations to generate real, explanatory hypotheses by providing sentence stems for them to fill in: “I expect to observe A because B.” Once students have filled in this sentence you can explain that B is a hypothesis and A is the expectation generated by that hypothesis.
• You can help students learn to distinguish between hypotheses and the expectations generated by them by regularly asking students to analyze lecture material, text, or video. Students should try to figure out which aspects of the content were hypotheses and which were expectations.

Summing up the process

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Scientific Hypothesis, Model, Theory, and Law

Understanding the Difference Between Basic Scientific Terms

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• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

Words have precise meanings in science. For example, "theory," "law," and "hypothesis" don't all mean the same thing. Outside of science, you might say something is "just a theory," meaning it's a supposition that may or may not be true. In science, however, a theory is an explanation that generally is accepted to be true. Here's a closer look at these important, commonly misused terms.

A hypothesis is an educated guess, based on observation. It's a prediction of cause and effect. Usually, a hypothesis can be supported or refuted through experimentation or more observation. A hypothesis can be disproven but not proven to be true.

Example: If you see no difference in the cleaning ability of various laundry detergents, you might hypothesize that cleaning effectiveness is not affected by which detergent you use. This hypothesis can be disproven if you observe a stain is removed by one detergent and not another. On the other hand, you cannot prove the hypothesis. Even if you never see a difference in the cleanliness of your clothes after trying 1,000 detergents, there might be one more you haven't tried that could be different.

Scientists often construct models to help explain complex concepts. These can be physical models like a model volcano or atom  or conceptual models like predictive weather algorithms. A model doesn't contain all the details of the real deal, but it should include observations known to be valid.

Example: The  Bohr model shows electrons orbiting the atomic nucleus, much the same way as the way planets revolve around the sun. In reality, the movement of electrons is complicated but the model makes it clear that protons and neutrons form a nucleus and electrons tend to move around outside the nucleus.

A scientific theory summarizes a hypothesis or group of hypotheses that have been supported with repeated testing. A theory is valid as long as there is no evidence to dispute it. Therefore, theories can be disproven. Basically, if evidence accumulates to support a hypothesis, then the hypothesis can become accepted as a good explanation of a phenomenon. One definition of a theory is to say that it's an accepted hypothesis.

Example: It is known that on June 30, 1908, in Tunguska, Siberia, there was an explosion equivalent to the detonation of about 15 million tons of TNT. Many hypotheses have been proposed for what caused the explosion. It was theorized that the explosion was caused by a natural extraterrestrial phenomenon , and was not caused by man. Is this theory a fact? No. The event is a recorded fact. Is this theory, generally accepted to be true, based on evidence to-date? Yes. Can this theory be shown to be false and be discarded? Yes.

A scientific law generalizes a body of observations. At the time it's made, no exceptions have been found to a law. Scientific laws explain things but they do not describe them. One way to tell a law and a theory apart is to ask if the description gives you the means to explain "why." The word "law" is used less and less in science, as many laws are only true under limited circumstances.

Example: Consider Newton's Law of Gravity . Newton could use this law to predict the behavior of a dropped object but he couldn't explain why it happened.

As you can see, there is no "proof" or absolute "truth" in science. The closest we get are facts, which are indisputable observations. Note, however, if you define proof as arriving at a logical conclusion, based on the evidence, then there is "proof" in science. Some work under the definition that to prove something implies it can never be wrong, which is different. If you're asked to define the terms hypothesis, theory, and law, keep in mind the definitions of proof and of these words can vary slightly depending on the scientific discipline. What's important is to realize they don't all mean the same thing and cannot be used interchangeably.

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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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• The Biology of Sex and Death
• 1.01 Scientific Methodology
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Scientific Methodology & Credible Sources

Learning objectives.

• Outline the general scientific method and give an example.
• Identify and describe the roles of basic elements of experimental design: dependent and independent variables, experimental treatments, positive and negative controls, sample size, and independent replicates.
• Be able to recognize the elements of experimental design in an example and to create your own experimental design that includes all the relevant elements.
• Explain the process of scientific peer-review and how to identify potential sources of bias in content found on the internet.

Introduction to Scientific Methodology

[modified from the khan academy ].

A biology investigation usually starts with an observation—that is, something that catches the biologist’s attention. For instance, a cancer biologist might notice that a certain kind of cancer can’t be treated with chemotherapy and wonder why this is the case. A marine ecologist, seeing that the coral reefs of her field sites are bleaching—expelling the algae that live inside them and provide energy resources—might set out to understand why.

How do biologists follow up on these observations? How can  you  follow up on your own observations of the natural world? In this article, we’ll walk through the  scientific method , a logical problem-solving approach used by biologists and many other scientists.

The scientific method

At the core of biology and other sciences lies a problem-solving approach called the scientific method. The  scientific method  has five basic steps, plus one feedback step:

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

The scientific method is used in all sciences—including chemistry, physics, geology, and psychology. The scientists in these fields ask different questions and perform different tests. However, they use the same core approach to find answers that are logical and supported by evidence.

Scientific method example: Failure to toast

Let’s build some intuition for the scientific method by applying its steps to a practical problem from everyday life.

1. Make an observation.

Let’s suppose that you get two slices of bread, put them into the toaster, and press the button. However, your bread does not toast.

2. Ask a question.

Why didn’t my bread get toasted?

3. Propose a hypothesis.

A  hypothesis  is a potential answer to the question, one that can somehow be tested and falsified. For example, our hypothesis in this case could be that the toast didn’t toast because the electrical outlet is broken.

This hypothesis is not necessarily the right explanation. Instead, it’s a possible explanation that we can test to see if it is likely correct, or if we need to make a new hypothesis.

4. Make predictions.

A prediction is an outcome we’d expect to see if the hypothesis is correct. In this case, we might predict that if the electrical outlet is broken, then plugging the toaster into a different outlet should fix the problem. Predictions are frequently in the form of an If-then statement.

5. Test the predictions.

To test the hypothesis, we need to make an observation or perform an experiment associated with the prediction. For instance, in this case, we would plug the toaster into a different outlet and see if it toasts.

If the toaster does toast, then the hypothesis is supported, which means it is likely correct.

If the toaster doesn’t toast, then the hypothesis is not supported, which means it is likely wrong.

The results of a test may either support or refute—oppose or contradict—a hypothesis. Results that support a hypothesis can’t conclusively prove that it’s correct, but they do mean it’s likely to be correct. On the other hand, if results contradict a hypothesis, that hypothesis is probably not correct. Unless there was a flaw in the test—a possibility we should always consider—a contradictory result means that we can discard the hypothesis and look for a new one.

6. Iterate.

The last step of the scientific method is to reflect on our results and use them to guide our next steps.

If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.

If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there’s a broken wire in the toaster.

In most cases, the scientific method is an  iterative  process. In other words, it’s a cycle rather than a straight line. The result of one go-round becomes feedback that improves the next round of question asking.

How is the scientific method used by biologists?

Quick recap: Biologists and other scientists use the  scientific method to ask questions about the natural world. The scientific method begins with an observation, which leads the scientist to ask a question. They then come up with a hypothesis , a testable explanation that addresses the question.

A hypothesis isn’t necessarily right. Instead, it’s a “best guess,” and the scientist must test it to see if it’s actually correct. Scientists test hypotheses by making predictions: if hypothesis  X  is right, then  Y  should be true. Then, they do experiments or make observations to see if the predictions are correct. If they are, the hypothesis is supported. If they aren’t, it may be time for a new hypothesis.

Hypotheses are tested using controlled experiments

What are the key ingredients of a controlled experiment? To illustrate, let’s consider a simple (even silly) example.

Suppose I decide to grow bean sprouts in my kitchen, near the window. I put bean seeds in a pot with soil, set them on the windowsill, and wait for them to sprout. However, after several weeks, I have no sprouts. Why not? Well…it turns out I forgot to water the seeds. So, I hypothesize that they didn’t sprout due to lack of water.

To test my hypothesis, I do a controlled experiment. In this experiment, I set up two identical pots. Both contain ten bean seeds planted in the same type of soil, and both are placed in the same window. In fact, there is only one thing that I do differently to the two pots:

• One pot of seeds gets watered every afternoon.
• The other pot of seeds doesn’t get any water at all.

After a week, nine out of ten seeds in the watered pot have sprouted, while none of the seeds in the dry pot have sprouted. It looks like the “seeds need water” hypothesis is probably correct!

Let’s see how this simple example illustrates the parts of a controlled experiment.

Control and experimental groups

There are two groups in the experiment, and they are identical except that one receives a treatment (water) while the other does not. The group that receives the treatment in an experiment (here, the watered pot) is called the  experimental group , while the group that does not receive the treatment (here, the dry pot) is called the  control group . The control group provides a baseline that lets us see if the treatment has an effect. Controls can be positive controls to demonstrate that the process or treatment actually works, or they can be negative controls , where no change should occur during the experiment.

Independent and dependent variables

The factor that is different between the control and experimental groups (in this case, the amount of water) is known as the  independent variable . This variable is independent because it does not depend on what happens in the experiment. Instead, it is something that the experimenter applies or chooses him/herself. Experiments can have more than one independent variable.  

In contrast, the  dependent variable  in an experiment is the response that’s measured to see if the treatment had an effect. In this case, the fraction of bean seeds that sprouted is the dependent variable. The dependent variable (fraction of seeds sprouting)  depends  on the independent variable (the amount of water), and not vice versa.

Experimental  data  (singular:  datum ) are observations made during the experiment. In this case, the data we collected were the number of bean sprouts in each pot after a week.

Variability and repetition

Out of the ten watered bean seeds, only nine came up. What happened to the tenth seed? That seed may have been dead, unhealthy, or just slow to sprout. Especially in biology (which studies complex, living things), there is often variation in the material used for an experiment – here, the bean seeds – that the experimenter cannot see.

Because of this potential for variation, biology experiments need to have a large sample size and, ideally, be repeated several times.  Sample size  refers to the number of individual items tested in an experiment – in this case,  1 0  bean seeds per group. Having more samples and repeating the experiment more times makes it less likely that we will reach a wrong conclusion because of random variation.

In fact, the beans in pots experimental design here has a major flaw. All the beans for each treatment are planted in the same pot. What if there’s an effect of the pot itself, its soil, or its location, that causes the beans in one to germinate better. A better design would be to plant each seed in its own pot, so that each seed is completely independent of the next seed. That independence for each sample is required to correctly use the statistical tests that biologists and other scientists also use to help them distinguish real differences from differences due to random variation (e.g., when comparing experimental and control groups).

Consider this example: A farmer wants to maximize her tomato crop yield and wonders if re-potting her plants in soil with various concentrations of nitrogen:phosphorus ratios will affect growth of tomato plants. After consulting the literature, she hypothesized that fertilizers high in nitrogen will produce fewer tomatoes, and fertilizers high in phosphorus will produce many tomatoes. Use this information to:

• Identify independent and dependent variables
• Provide suggestions for independent variable
• Suggest possible positive and negative controls
• Sketch a graph for this experiment
• What are some other variables that could affect the tomato crop? Suggest a follow-up experiment with a research question, hypothesis, independent, dependent, and control variables.

Credible Sources and how to find them

These days, when we have a question, we turn to the internet. Internet search engines like Google can link you to almost any content, and they even filter content based on your past searches, location, and preference settings. However, search engines do not vet content. Determination of whether content is credible is up to the end-user. We are also living in an era where misinformation can be mistaken for fact. How do we know what information to trust?

The process of scientific peer-review is one assurance that scientists place on the reporting of scientific results in scientific journals like Science , Nature , the Proceedings of the National Academy of Science (PNAS), and many hundreds of other journals. In peer review, research is read by anonymous reviewers who are experts in the subject. The reviewers provide feedback and commentary and ultimately provide the journal editor a recommendation to accept, accept with revisions, or decline for this journal. While this process is not flawless, it has a fairly high success rate in catching major issues and problems and improving the quality of the evidence.

In the rare situation when a study has passed through the sieve of peer review and is later found to be deeply flawed, the journal or the authors can choose to take the unusual action to retract the work. Retraction is infrequent but does happen in science, and it is reassuring to know that there are ways to flag problematic work that has slipped through the peer review process. A  prominent example of a retracted study in biology was one linking the MMR vaccine to autism ( https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2831678/ ). Some sectors of the public still have misconceptions about supposed linkages between autism and vaccination. The putative autism-vaccination connection is a classic case of how correlation does not imply causation , meaning that just because two events co-occur—the recommended childhood vaccination schedule and the onset of autism symptoms—does not mean that one caused the other.

With the rapid spread of SARS-CoV-2 and the Covid-19 pandemic, the demand for scientific information about the novel coronavirus SARS-CoV-2 outpaced the rate that journals can peer-review and publish scientific research. Many research articles for SARS-CoV-2 have therefore been released as preprints, meaning they have been submitted to a journal for peer-review and eventual publication, but the authors wanted to release the information for immediate use.

In the media, journalists use published and preprint articles, press releases, interviews, and public records requests, and other sources to find source information. They cite their sources when possible and are responsible to their editors for the quality and authenticity of their reporting. Some media sources have better track records than others for unbiased reporting.

Websites and social media posts are places where anyone can post anything and make claims that are or are not supported by evidence.

As the end-users, our job is to find sources supported by evidence, cited ethically, and otherwise credibly presented. We have the responsibility to notice whether an organization is funded or motivated in ways that might generate bias in their content. We have the obligation to cross reference ideas from unvetted sources to help us establish how believable or how credible the source of information is. Science is based in evidence, and we will work this semester to identify and interpret scientific evidence.

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The Scientific Hypothesis

The Key to Understanding How Science Works

Karl Popper – The Short Course

The philosopher, Sir Karl Popper, author of The Logic of Scientific Discovery , and Conjectures and Refutations , proposed an evolutionary view of knowledge as a constant tension between present understanding, tests of that understanding, and proposals for better understanding.

As far as modern science is concerned, Karl Popper is the most influential philosopher of the 20 th century, maybe of all time.  Although many scientists know his name, not all are really familiar with his thinking.  Here is a brief list of his key points.

1.  The main goal of empirical science is to discover the Truth about nature (Popper doesn’t use a capital ‘T’ but it conveys the very large conception of truth that he has in mind).

2.  However, science can never actually get to the Truth, even in principle.  The Truth would have to hold for all times, all conditions, and all places in the Universe – so attaining Truth is not an option.

3.  Uncertainty is therefore a defining characteristic of scientific knowledge and Popper’s philosophy takes this principle seriously:  If all knowledge is fundamentally uncertain, what do we “know”?  How can science make progress (as it demonstrably does)?  What are reasonable grounds for taking action?

4.  Scientists propose explanations that they think are true (small ‘t’ to indicate the modest, everyday kinds of truths), hypotheses, and test them “severely,” trying to see if they are actually false. 

5.  A severe test is one that has a good chance of finding that the hypothesis is false if it really is false.  If a hypothesis is false; we reject it.  If it isn’t false, we accept it as provisionally true.  At any time we can test it again and maybe reject it later, but its acceptance is always provisional and, probably, temporary. 

6.  We want to know if the hypothesis could be false because we don’t want to hold false ideas. It’s generally easy to find a test that will produce results that should be consistent with a hypothesis, so these tests are not very informative.

7.  Philosophers used to think that “induction” (or inductive reasoning) could lead to True scientific facts and, although no one really believes this today, some people still suggest that induction is a process of inferring reliable facts from regularities in nature.  Popper rejects this idea, using the famous example that, no matter how many white swans you see, you can never be certain that “all swans are white,” whereas (repeatedly and reliably) observing one genuine black swan would let you reject the hypothesis that “all swans are white.” So scientists must actively look for black swans and not white ones (that’s why a black swans are featured here).

8.  If a hypothesis passes a severe test then we still don’t know if it is True (we’ll never know that) but it is reasonable to feel a bit more confident about it than we did before the test.  Popper says the hypothesis has been “corroborated” by the test (not “confirmed” because many people tend to think of a confirmed statement as a True one).  “Corroboration” looks at past performance, which we know a lot about; “Confirmation,” (or “verification”) often implies elements of lasting truth, which we’re not justified in assuming.

9.  If we need to take action, then it is perfectly reasonable to act on the basis of the best-corroborated hypothesis that we have. After all, the hypothesis was proposed as a true explanation for a phenomenon, and if its passed all the severe tests its been exposed to then there is no reason to think it is not true .   

10.  A fundamental distinction is between the way basic and applied science look at corroborated hypotheses.  Applied science must necessarily be satisfied with the best-corroborated hypotheses when it invests millions of dollars in creating its advances.  Basic science is never fully satisfied even with well-corroborated hypotheses and, even when its knowledge advances are built on them, basic science never assumes that they are unquestionably true.  Basic science is always ready to re-examine and re-test even its most well-established “facts.”

11.  Our scientific knowledge consists of those hypotheses that have been tested and corroborated (i.e., not found to be false); these are the “facts.”

12.  Since nothing can be certain in science, even apparent falsifications may be in error.  An openness to changing our minds, a willingness to consider all possibilities, is therefore a defining trait of science.  “Trial and Error” is Popper’s motto. 

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Scientific conclusions need not be accurate, justified, or believed by their authors

• Original Research
• Open access
• Published: 21 April 2021
• Volume 199 , pages 8187–8203, ( 2021 )

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• Haixin Dang   ORCID: orcid.org/0000-0002-8039-4876 1 &
• Liam Kofi Bright 2  

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We argue that the main results of scientific papers may appropriately be published even if they are false, unjustified, and not believed to be true or justified by their author. To defend this claim we draw upon the literature studying the norms of assertion, and consider how they would apply if one attempted to hold claims made in scientific papers to their strictures, as assertions and discovery claims in scientific papers seem naturally analogous. We first use a case study of William H. Bragg’s early twentieth century work in physics to demonstrate that successful science has in fact violated these norms. We then argue that features of the social epistemic arrangement of science which are necessary for its long run success require that we do not hold claims of scientific results to their standards. We end by making a suggestion about the norms that it would be appropriate to hold scientific claims to, along with an explanation of why the social epistemology of science—considered as an instance of collective inquiry—would require such apparently lax norms for claims to be put forward.

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1 Introduction

We hope that by inquiring together we may eventually discover the truth. Or, failing that, at least to achieve some other epistemic aim—we shall assume it is truth from here on out, though nothing turns on that choice of aim in particular. Whatever it may be, to achieve our long run epistemic goals, individuals or small teams must periodically make local contributions. The field of biology, say, has succeeded in its aim if it discovers the truth about biological systems, but what does this entail for how we evaluate individual papers published in biology journals? Should we say each of those must discover the truth about its particular topic matter? In general, if we are to make progress in collective inquiry, what do we require from the individual contributors?

Even if we grant that the relationship between our expectations for the whole process and our expectations for individual inputs must be complex (see Mayo-Wilson et al., 2011 ), we evidently hold such individual contributions to some standards. The process of peer review as it now exists (on which see Heesen & Bright, 2020 ) seems to presuppose that there are conditions under which it would be inappropriate to bring a conclusion to the collective attention of one’s field. Likewise, the very fact that many consider widespread replication failure (see e.g. Open Science Collaboration, 2015 ) to be problematic suggests that they hoped for more from the principal claims of those fields in which that debate is happening. So what exactly are those norms which, in the good case, a particular local piece of scientific inquiry will satisfy in putting a conclusion forward?

To answer this question, we turn for inspiration to a branch of analytic epistemology and philosophy of language that concerns itself with the conditions under which particular claims are properly put forward. We will understand “properly put forward” here to mean uttered in a way that coheres with the contextually appropriate utterance norms. It is often claimed that assertions are utterances held to certain norms, called norms of assertion. This does not mean that in our assertions we always (or even usually) meet the appropriate norms. But rather that assertions qua assertions are improper if they fail to satisfy the requirements set by these norms—indeed in some cases it is felt that it is constitutive of something being an assertion that it is held to these norms.

In this paper, as a way into the general question of how individual pieces of inquiry may contribute to a successful joint endeavour, we explore whether the utterances made by scientists when announcing their findings and results are held to any of these clusters of norms of assertion. The analogy between assertions and discovery claims in scientific papers seems natural—intuitively, they are both ways of putting forward a claim about how the world actually is. So as a starting place for inquiry, it seems a good place to begin in thinking about what sort of norms one should satisfy in putting a claim forward. Take, for instance, a discovery claim drawn from the abstract from (at time of writing) a paper in the latest issue of the journal Cell . The authors claim that their results “demonstrate'' that “loss of Cx3cr1 in CNS-myeloid triggers a Cxcl10-mediated vicious cycle, cultivating a br-met-promoting, immune-suppressive niche” (Guldner et al 2020 ). On its face, to claim to have demonstrated something is to claim that it has been shown to be true and what is being claimed true constitutes an assertion about how the immune system in the brain operates. However, we will find that such claims made about individual scientific contributions ought not be held to typical norms of assertion. We do not take a stand on whether the scientific conclusions should be held to be assertions or not, but we do claim that reflection upon why they are not held to typical norms of assertion is illuminating as to the general character of collective inquiry and the relationship its individual subcomponents must bear to the overall endeavour.

There are three clusters of norms in the norms of assertion literature. The first cluster of norms studied by analytic epistemologists are the factive norms. Most famously there is the knowledge norm, owed to Timothy Williamson. According to this norm "[o]ne must: assert p only if one knows p" (Williamson, 2000 : 243). In a similar vein García-Carpintero ( 2004 ) proposes the norm that one must assert p only if one's audience comes thereby to be in a position to know that p (156). Matthew Weiner, on the other hand, (2005), proposed a truth norm: assert only what is true. Jason Stanley has raised the suggestion that one may assert that p only if one knows that p (or is in a position to know that p) on the basis of evidence that gives one the highest degree of justification for one's belief that p. (2008: 35). MacFarlane ( 2014 : 108) proposes a norm that one ought to retract an (unretracted) assertion if it turns out not to be true. This first class of proposals is united in holding assertions improper if they are not true.

The second cluster are justification norms. For instance, Jennifer Lackey, reflecting on what she calls "self-less assertions" in which one properly asserts what one does not believe, proposes that assertions are held to the norm "that it is reasonable to believe that p, and that the speaker asserts that p for this reason (even if in fact not believing that p)" (quotes from Pagin, 2016 ; summarising Lackey, 2007 ). Gerken ( 2015a ), reflecting specifically on lab mates or collaborating scientists discussing ongoing research, advocates (in that context) a norm wherein scientists must be “discursively justified”—justified in their claims, and able to explain that justification to any who request reasons. Kvanvig ( 2009 : 145, 2011 : 249–250) has proposed that the norm of assertion is that one needs to be justified if one asserts, and that the relevant concept of justification is such that it is sufficient for knowledge, provided the proposition is both believed and true (and the justification is undefeated).

The third cluster are belief norms. Taking a cue from Grice ( 1961 ), Bach ( 2008 ) argues that the only relevant rule on assertion is belief: “In the case of assertion, to be sincere is to hold the belief one expresses. So we can say that belief, not knowledge, is the rule on assertion” (77). Others have argued that it is only proper to assert what one believes justifiedly. Douven ( 2006 ) defends the view that one should only assert what one believes to be rationally credible.

We ground our inquiry in an exploration of whether norms of any of these types should be applied to scientific claims, and if not, why not. The aim of this paper is not to entirely dismiss the proposed norms of assertion as they are currently studied by analytic epistemologists. Rather, our aim is to demonstrate that these norms of assertion are not appropriate for important classes of scientific claims. In the next section we will say more about the sorts of claims we wish to study, and give a simple abstract example of such claims being made. We then argue that the points illustrated in this abstract case can also be studied in a more detailed historical case, and that we can generalise our lessons to modern scientific practice. We will give two different theoretical arguments for this: an argument from division of cognitive labour and an argument from the pessimistic meta-induction.

2 Public avowals in science

To be more precise about the target of this paper, we will call the class of utterances that we are interested in “public avowals.” These are utterances made by scientists aimed at informing the wider scientific community of some results obtained: conclusions in papers in peer-reviewed journals, conference presentations, posters, online pre-prints, etc. Indeed, these are the kind of utterances which we most commonly associate with the scientific community. Open up an issue of Science , for example, and one may read that “early vertebrate clades, both jawed and jawless, originated in restricted, shallow intertidal-subtidal environments” (Sallan et al., 2018 ). Public avowals may also be much more speculative, hesitant, or demure, such as this highly qualified statement appearing in the abstract of a recent paper in the American Journal of Sociology : “[a]pparent changes in disparities across census tracts may result partly from a higher level of sampling variation and bias due to the smaller sample” (Logan et al., 2018 ).

We do not mean by public avowals, therefore, definitive statements about the world that are confidently maintained. Instead, we simply mean claims that scientists feel in a position to put forward as a conclusion of their research. The conclusion claims of scientific papers are sometimes put forward in a qualified or hedged manner, but they are still statements that publicly report the results of research. Thinking about the norms or standards these public avowals are or ought to be held to is informative in telling us how to think about the central contributions of novel scientific papers.

Gerken ( 2015b ) has argued that testimony between collaborating scientists during the process of collaboration are held to special epistemic norms. He argues that such intra-scientific testimony ought to be held to the standard of discursive justification. The kinds of utterances we are concerned with differ from Gerken. We are interested in the norms that govern inter-scientific testimony: claims made by scientists or collaborations aimed at the wider scientific community. These types of claims are meant to convey the products of scientific inquiry, rather than made during the course of that inquiry, and are made in public venues where the primary audience is other scientists. The paradigmatic cases of inter-scientific testimony are scientific articles published in specialist journals aimed at other expert scientists (for norms concerning co-authoring scientific papers see Huebner et al., 2017 ; Bright et al., 2018 ). This is because our focus is on the question of how individual contributions are made to a larger project of scientific inquiry; it is hence the relationship between an individual scientist and her broader scientific discipline that most concerns us.

Public avowals in science should not be confused with extra-scientific testimony or “public scientific testimony.” Statements from scientists aimed at policy-makers are not the type of utterances we are interested in. It is important to distinguish between claims aimed at the scientific community and claims aimed at the general public or policy-makers. The IPCC assessment report on climate change, for example, while made publicly, is primarily aimed at political bodies, and such testimony is properly held to a different standard. Extra-scientific testimony is hence not the target of our paper.

It is very important that we recognize that “public scientific avowals” are held to different epistemic norms than that of “public scientific testimony.” Confusing “public scientific avowals” and “public scientific testimony” can lead to disastrous misunderstandings about science communication. To anticipate some results of our argument, if a layperson were to only follow the specialist journals, the layperson may come to the conclusion that scientists often report false things, after reading many contradictory avowals made at the conclusion of competing cutting-edge research papers. However, it would be a mistake for the layperson to think that when scientists make reports to policy-makers that these reports also have the same epistemic status as papers in scientific journals written for a primarily specialist audience.

We will now argue that public scientific avowals should not be held to factive norms, justification norms, and belief norms. That is to say, we will argue that public avowals which violate all such norms are entirely appropriate in scientific inquiry. Our argument depends on how scientific findings are actually communicated in the scientific community, or the role such communications play. To illustrate the role of public scientific avowals in science, consider this scenario which a scientist may often find herself in:

Zahra is a scientist working at the cutting edge of her field. Based on her research, she comes up with a new hypothesis. She diligently pursues inquiry according to the best practices of her field for many months. Her new hypothesis would be considered an important breakthrough discovery. Zahra knows that many more studies will have to be done in the future in order to confirm her hypothesis. Further, she has read the current literature and realizes that the existing research in her field does not, on net, support her hypothesis. She does not believe that she has conclusively proven the new hypothesis. Nonetheless, Zahra sends a paper reporting her hypothesis to the leading journal in her subdiscipline. In the abstract of the paper, the conclusion, and talks she gives on her work, she advocates for her hypothesis. Peer reviewers, while also sceptical of the new hypothesis, believed that her research had been carried out according to best known practices and her paper would be a valuable contribution to the field. Her paper, which purports to have advanced a new hypothesis, is published and widely read by members of her community. In subsequent years, additional research in her field conclusively demonstrates that Zahra’s hypothesis was false.

Public scientific avowals like Zahra’s maintaining her hypothesis do not live up to the standards set by the norms of assertion. Zahra’s avowals were false. Her avowals were not justified by the total evidence available to her, since she is acquainted with the existing research in her field which does not support her hypothesis. Furthermore, Zahra herself did not fully believe in her avowals. Nonetheless, we believe that some such avowals that fail norms we hold assertions to can still be important to the epistemic success of science. In fact, Zahra’s conduct is exactly how scientists ought to act in order to successfully communicate scientific findings. During active scientific research, public scientific avowals will often fail to meet the norms of assertion, yet scientists still need to continue to make avowals which report their findings to other members of their community.

How widespread and generalizable are public scientific avowals that fail to meet the norms of assertion? Next, we present a short case study from the history of physics, which further illustrates how public avowals actually function among scientists. Scientists do sometimes report false, unknown and unjustified things that they do not believe, and this is an acceptable part of the scientific process.

3 Case study: scientific avowals during periods of active inquiry

In the early twentieth century, physicists were discovering a variety of new physical phenomena which were known as radioactivity. There was intense research into the true nature of the mysterious products that were emitted from cathode ray tubes: were they particles or waves? Of the cathode ray products, X-rays and γ-rays were particularly perplexing. It was immediately discovered that X-rays reacted with photographic plates, suggesting that they were a form of light, which would hence suggest they are wave phenomena. However, they also reacted with other materials like particles. On the other hand, γ-rays emissions were almost always accompanied with α and β-particles. It was an open question whether γ-rays and X-rays were material—that is, made up of particles—or whether they were waves in the electromagnetic aether. Investigations into the perplexing properties of all these forms of radioactivity were a major research program in the early twentieth century. Many different theories were proposed to account for the conflicting experimental data.

An early researcher in radioactivity, William Henry Bragg proposed a neutral material particle theory of γ and X-rays, which he defended in a series of scientific articles from 1907 to 1912 (Bragg, 1907 , 1908a , 1908b , 1908c , 1910 , 1911 , 1912 ). According to this material theory, γ and X-rays were in fact made up of positively charged α-particle and a negatively charged β-particle bound together, making a neutral pair . Bragg argued that the leading theory at the time, the aether pulse theory (a form of wave theory), could not fully explain his experimental results regarding γ and X-rays. An aether pulse was thought of like a disturbance or ripple in the electromagnetic aether. Bragg published a paper in 1907 in the leading physics journal of the day, first criticizing the available evidence of both γ and X-rays as being an aether pulse, and also advancing his own material theory of neutral pairs:

It appears, therefore, that all the known properties of the γ rays are satisfied on the hypothesis that they consist of neutral pairs. (Bragg, 1907 , 441) To sum up, it is clear that a stream of X rays contains some æther pulses, but it is not easy to explain all the properties of X rays on the æther-pulse theory. The explanations are easier if the rays are supposed to consist mainly of neutral pairs; and the existence of such pairs is not improbable a priori. (Bragg, 1907 , 448).

Bragg made clear public avowals to the effect that both γ and X-rays had been shown to be material, such as in a 1908 letter to Nature :

Meanwhile, I will point out that the experimental proof of the material nature of the γ rays carries with it, almost surely, a corresponding proof as regards the X-rays. (Bragg, 1908a , 270)

But when pressed on the virtues of the competing aether pulse theory by fellow physicists, Bragg follows up in another 1908 letter to Nature :

If I admit the existence of ether pulse, I do not thereby weaken my contention that the most important and effective part of γ and X ray radiation is material. (Bragg, 1908b , 560)

In these series of articles and letters to Nature (see also Bragg, 1908c ), Bragg is putting forward to the scientific community his theory that both γ and X-rays are material particles. His avowals here are not definitive, as Bragg often presents his hypothesis as a conditional or qualifies his statements with hedges like “almost surely.” Nonetheless, over the course of several years, Bragg published extensively defending his hypothesis against alternatives, pointing out virtues of his view over others, and was clearly putting forward his hypothesis as the conclusion to be drawn from his experiments on γ and X-rays.

Bragg’s public avowal of the neutral material particle theory did not satisfy the norms associated with assertion. First, the neutral material particle theory was false. γ and X-rays are not material particles. γ and X-rays are not made of neutral pairs, consisting of negatively and positively charged particles bound together. Even in 1907, Bragg theory was viewed as being implausible by scientists in the physics community (Wheaton, 1983 , 94). A few years later, in 1912, a decisive experiment was conducted by diffracting X-rays through crystals that settled the matter altogether. X-rays cannot be made up of material particles if they can be diffracted; X-rays must be waves. Today, physicists believe that both γ and X-rays are forms of light, composed of photons that exhibit wave-particle duality. There is no aether.

Furthermore, Bragg’s public avowals were not justified on the total evidence, on balance, available at the time. In his early 1907 paper Bragg knew full well that his theory went against the available evidence at the time. Previous scattering experiments from Charles Barkla in 1905 have been taken as corroborating evidence for the pulse theory. He knew he was opposing a "widely accepted" and "ably advocated" theory of γ and X-rays as aether pulses (Wheaton, 1981 , 1983 ). According to Bruce Wheaton, one of the leading historians on this historical episode, the total evidence, on balance, was not on Bragg’s side:

While Bragg had been developing his novel hypothesis, Barkla's experiments had turned up new and remarkably homogeneous secondary x rays. In its fully interpreted form, this evidence would prove to be virtually unanswerable by Bragg. (Wheaton, 1983 : 90)

Bragg was well acquainted with Barkla’s criticisms and corresponded with him about his experimental results throughout this period—they in fact exchanged a long chain of public letters in Nature at this time. Barkla’s results on both scattering and secondary X-rays cannot be accounted for on Bragg’s theory. So Bragg’s avowals cannot be justified on the total evidence available to him.

Finally, Bragg did not believe in his theory. This is more apparent in Bragg’s personal letters. In 1910, Bragg had been corresponding with Arnold Sommerfeld, a leading German physicist. While Bragg had continued to publicly defend his particle theory, in private, Bragg was more candid about how he considered his theory:

I am very far from being averse to a reconcilement of a corpuscular and a wave theory: I think that some day it must come… I have suggested the neutral pair form myself: but I do not wish to press this unduly or be dogmatic about it. It seems to me to be the best model to be devised at present, and I have no right to claim more. (reproduced in Wheaton, 1981 : 272)

Here, Bragg appears to admit that he is only committed to the neutral pair theory as a “suggestion” or the “best model at present.” Dogmatic belief in the neutral pair theory was untenable because shortly after Bragg’s first proposal of the theory, the α-particle was ruled out as a possible candidate for the positive component of the neutral pair. Bragg had to defend the possibility of an unknown positive particle as well as the neutral pair itself. The neutral pair theory was difficult to defend in face of mounting evidence against material theories, which is why Bragg was so circumspect about his doxastic state in his private letters.

While Bragg’s public avowals about the neutral pair theory of γ and X-rays were false, unjustified on total evidence, and were not believed, we want to maintain that these avowals did not violate the norms of inter-scientific communication. Bragg, after all, was awarded the Nobel Prize in 1915 for his work on X-ray crystallography, a new field born out of his early work on the nature of X-rays. Furthermore, Bragg’s X-ray publications were widely read and discussed by other physicists and spurred decades of important research around X-rays. Bragg’s biggest critic, Charles Barkla, would later win the Nobel Prize in 1917 also for his own work on X-rays scattering, which were in part driven by Bragg’s provocative writings.

Let us summarise, then, what this historical episode demonstrates. In a series of scientific public avowals Bragg defended a theory, the neutral material particle hypothesis. His conduct over the course of this research was felt to be sufficiently meritorious that it did not interfere with him winning the highest honour a scientist may achieve—the false theory spurred valuable research into the diffraction patterns of X-rays. Throughout this period, however, many of the avowals he made were false; by the time the Nobel was awarded this was certainly known to the physics community. As Bragg himself admits the full weight of evidence was not clearly on his side, and for this reason he was throughout privately quite circumspect about the extent to which he even believed his claims about the neutral pair. His public avowals, therefore, were neither true, nor justified by the total evidence available, nor believed to be either true or justified by Bragg himself. In the coming sections we shall argue that far from being unusual or evidence of some epistemic defect, this is a proper state for scientific public avowals.

4 Norms for public avowals

The proper social epistemic functioning of science requires that public avowals in science fail to satisfy the norms we surveyed at the beginning. We take it to be uncontroversial that if there will be sustained and organised inquiry, any collective inquiry of the sort that science in fact is, there must be successful communication of the latest results and ideas about them. We will assume that the norms governing utterances communicating those results and ideas must reflect and endorse this state of affairs. Any norm for making avowals which rendered it impermissible to put forward just those claims which constitute necessary features of collective inquiry would be misaligned with the purpose of the activity it is supposed to govern. The scientific community has developed norms, both explicit and implicit, which govern utterances that are appropriate for communicating scientific findings to other scientists. We shall argue that these do not look like any of the purported norms of assertion, and for good reason.

First, note the role of the division of cognitive labour. It is well recognised in philosophy of science that such a division is an important strategy for collective inquiry (Kitcher, 1990 ; Strevens, 2006 ). Scientists invest time and resources on different approaches to a research domain. For example, during early investigations into γ and X-ray behavior, different researchers used different techniques to study γ and X-ray behavior. This division of labor is often not explicitly planned; rather the limited resources, credit and incentive structures of science encourage scientists to pursue new avenues of research and use different methodologies (see Kummerfeld & Zollman, 2015 ; Zollman, 2018 ).

This division of cognitive labor means that during periods of active inquiry, scientists will often be publishing discoveries which are seemingly in conflict with each other. This in fact occurred during the γ and X-ray episode. Bragg investigated the particle-wave problem using, what we call today, hard X-rays, which are higher in energy. Charles Barkla (Bragg’s leading critic and fellow Nobel laureate) investigated using, what we call today, soft X-rays, which are lower in energy. This is because Bragg and Barkla used slightly different techniques for producing X-rays, due to the limited availability of radioactive source materials. They also used different experimental techniques to look at X-ray behavior (see Wheaton, 1983 ). This led them to come to radically different conclusions about the nature of X-rays. Bragg’s experiments seemed to show that hard X-rays behaved like particles, because he observed large transfers of energies which is indicative of a particle hitting others. Since γ rays are even higher in energy than X-rays, Bragg was able to observe particle-like behaviors. Barkla, on the other hand, observed his soft X-rays to behave more consistently like waves.

At most one of these models of X-rays could have been correct (as it turned out neither are correct), yet both scientists had to publicise their work if scientific progress were to be made. Public avowals which break factive norms are thus epistemically beneficial and ought to be encouraged. Without them we would have never found out eventually that how X-rays are produced changes the energy of the X-rays—this was a fact we only discovered because Bragg and Barkla came to different results due to how they were generating X-rays in their labs.

Moving beyond the necessity of the division of labour, the case study can be placed in the context of a broader historical argument. This builds on the kind of observations that inspired the pessimistic meta-induction (which typically focused on physics) and Stanford’s ( 2006 ) problem of unconceived alternatives (which focused on the history of biology). This can be supplemented by recent work on replicability in psychology (Open Science Collaboration, 2015 ) and cancer research (Begley & Ellis, 2012 ). Frequent false public avowals are a necessary part of scientific progress, especially in areas of active inquiry. While scientific realists have argued that the history of science is also full of epistemic successes (for example, see Fahrbach, 2011 , 2017 ), it is nevertheless the case that individual contributions to science in areas of active inquiry are prone to error and are continuously replaced by new errors.

These historical arguments can be supplemented with theoretical arguments. All our inquiry inevitably takes place in a situation of scarce resources and competing demands on our attention. Where this is the case, we have theoretical reason to believe that scientists will not gather as much data as a disinterested epistemic planner would have them gather (Heesen, 2015 , 2018 ). Further, some of our inquiry takes place in contexts where there are weak signals, sparse data, and considerable difficulty in running replications. When this is so, limitations to what is possible through statistical inference give us reason to suppose that most of our published findings will be false (Ioannidis, 2005 ; Romero, 2016 ).

These facts together suggest we have not found a better way of communicating findings than one in which there are frequent false avowals. As the X-ray case shows, it is crucial for false theories to be put forward, so to encourage more research; Bragg’s neutral pair theory of X-ray was directly responsible for sparking interest in studying radioactivity among many other physicists. If we agree with versions of the pessimistic meta-induction wherein new scientific theories tend to demonstrate pervasive reference failure in their previous iterations, it means that almost all scientific public avowals turn out to be false.

What does the necessity of dividing labour, and the pessimistic conclusions about present and past science, tell us about our norms for scientific public avowals? We think they quickly rule out factive norms. For factive norms would judge inappropriate a great part of those avowals that are necessary for the progress of any collective inquiry which must divide labour and proceed by correcting errors. It is not just that we in fact often will say false things in the course of inquiry, but rather that inquiry could not proceed in a way that was even remotely successful if we did not do so. Given the ubiquity and necessity of such things to collective inquiry this renders factive norms simply inappropriate as a means of deciding what conclusions ought to be put forward from individual contributions to the wider project. The norms of inquiry should not rule out necessary parts of the process.

In fact, we note, there have been many who have argued that factive norms would not even be appropriate for reporting the results of “completed science”, whatever that might be. Consideration of the nature of physical laws (Cartwright, 1983 ), scientific models (Frigg & Nguyen, 2016 ), or what it would be to achieve understanding through science (Dellsén, 2017 ; Elgin, 2007 ), have all led philosophers to conclude that the terminus of scientific inquiry need not be taken to be possession of true beliefs. Avoiding requiring adherence to factive norms in the midst of inquiry thus looks even more attractive when one considers that it may not even be the desired end state.

With one more assumption we think the arguments just reviewed also rule out justification norms as appropriate for governing individual contributions to collective inquiry. Our additional assumption is that whatever notion of justification is at play here it meets the total evidence requirement (Good, 1967 ). Our total evidence includes the information just provided about what kind of process science is. Not just how generally reliable our inquiry is, but also how reliable it is for hard problems in particular, and how reliable we are ourselves as individual or communal epistemic agents. For any one paper, the reasons one could produce in favour of its central claim could well be outweighed by these competing second-order considerations. Hence if one is to communicate the results of sustained inquiry on hard problems then one cannot limit one’s avowals to those a total-evidence respecting epistemic norm would permit. In the X-ray case, Bragg knew that the total evidence, on balance, did not support his corpuscular theory, but he nonetheless published and defended it publicly. This was not condemned by his contemporaries, rather they took his theory seriously despite everyone being aware that there exists compelling research which did not support it. So the surveyed justification norms of assertion cannot govern scientific avowals if they are to foster and permit publishing surprising findings or results, and we believe would not appropriately do so for any collective inquiry into a difficult or obscure matter.

Finally, one might think that none the less the belief norms may apply to public avowals. For all that has just been said, scientists may believe their claims to be true or justified, as long as scientists are ignorant of this history or these theoretical results. Or, perhaps, as long as they retain their faith in their own particular claims even while being aware that in general studies like theirs are not reliable. So long as scientists can maintain belief in themselves, we could require they only avow when they believe all they have said. Indeed, the International Committee of Medical Journal Editors might be requiring this when it says “[a]ll members of the group named as authors… should have full confidence in the accuracy and integrity of the work of other group authors” (ICMJE, 2013 : 3).

We think such a retreat to mere belief or second order belief would be a mistake. For one thing, there is a large and persuasive literature in the philosophy of science detailing situations wherein inquiry may proceed well without scientists believing their claims (e.g. Kapitan, 1992 ; Dawes, 2013 ; Cabrera, 2018 ; Dellsén, 2018 ; Palmira, 2020 , Fleisher, 2020 ). But over and above this we do not think that bad faith can be required of people. Quite generally we do not think a good social system can require participants to be ignorant as to the nature of their activities and their history, or require that they reason irrationally upon being informed about these things. False ideology or absurd arrogance should not be a prerequisite for inquiry. In this context it means that we cannot require as a condition of successful public avowal in science that scientists may not learn the various historical or theoretical facts that would undermine their faith in their assertions. Scientists do not need to believe that they are epistemically special in order to successfully participate in science.

If one agrees that bad faith cannot be mandatory then one can move from the above arguments to a rejection of both the justification norms and the belief norms. Scientists may well know of themselves that they are engaged in an activity which is not reliable for the kind of results they report. They thus may well not think their investigations are sufficient reason to believe their conclusions, or would suffice for justification all things considered. Their total evidence (inclusive of second order evidence) could not justify this, and they form their credences appropriately. Hence they may not believe their results at all, or believe them to be justified, and yet still properly avow. We ought not rule out as inapt these avowals of scientists who take the full measure of their epistemic situation. Hence building on the social epistemology of dividing cognitive labour, the pessimistic meta-induction and related theoretical results, we have been able to generalise our case study. This ruled out the factive, justification, and belief norms as the proper means of deciding what sort of conclusions are appropriately put forward as individual contributions to collective inquiry.

In some sense, of course, one could avoid this by just making weaker claims. Rather than saying X causes Y, one could say that according to one’s data X causes Y. The weaker claim might well be true, or at least justified even in light of total evidence, and perhaps ought to be believed in any case. Certainly it is the case that many scientific result claims are made in a hedged way in something like this fashion. Of course, at present we do not think that scientists always obey this norm of making only such weaker statements. However, if one was a strong advocate of one of the norms surveyed one could insist that strictly speaking only such weaker avowals would be proper, and fault those scientists who do not live up to this.

We note this possibility simply to note that it is a path we shall not explore here. We are concerned here with what should be expected of individual contributions to a broader inquiry, not really the exact forms they take. We are only making an analogy to the norms of assertion literature, not trying to weigh in on speech act theory. In the context of scientific research, we cannot only concern ourselves with events in a particular laboratory, or evidential relationships between particular data sets and a proposition. We also must put forward candidates for scientific communal uptake and potential targets for future inquiry. This goes all the more when one considers, as we have largely set aside, that one wishes ultimately to make use of scientific claims as the basis of public policy, where their external validity is what is of paramount importance (c.f. Cartwright, 2012 §4).

We do not doubt that it would be possible to reform scientific communication behaviour such that one sticks to scientific public avowals that are proper according to one of the surveyed norms by insisting on appropriate hedging. One could then just say explicitly (perhaps in so many words): “we also suggest such and such as a candidate for further investigation.” Or one could understand it to be an implicature that if Lab 1 reports that its inquiry suggested that X causes Y, Lab 2 might profitably investigate whether its own inquiry would suggest as much also. But our concern is not really with the precise linguistic form such claims would take so much as the social uptake amongst scientists. However results are conveyed, scientists must decide what claims are worthy of further tests (Friedman, 2020 ; Thorstad, 2021 ). Our point is that it would be inappropriate for scientists to insist that (in the absence of fraud or mistake or misfortune) these pursuit worthy claims must be true, or justified, or believed to be as much by their proponents. Further, we shall briefly outline a proposal for an actual norm that could appropriately govern, and is perhaps governing, scientific avowals in the next section, based on an analysis of the social epistemology of science. If one understands what norms avowals must satisfy in the good case, one can better adjust one’s attitude to them, without needing scientists to change their manner of communication.

5 Primacy of the social

The upshot of the above is that scientific public avowals can, and in some cases ought, be allowed to be proper even when they are false, not justified in light of our total evidence, and neither believed to be true nor justified. While we are most concerned to show that this is normatively appropriate, we think this both matches the actual practice and standards of science and facilitates the enterprise of collective inquiry perpetuating itself successfully. The rejected norms were picked as the basis of our inquiry since they had been found plausible or defensible as norms for assertion, which is at least a somewhat related activity of putting forward scientific public avowals. So why this discrepancy? In short, we think this is because the social enterprise of inquiry requires that we allow people to be more lax in certain contexts than we normally require of individuals offering testimony, and through a long process of cultural evolution the scientific community developed norms of avowal to accommodate that fact.

It may seem surprising that the norms in science are less, rather than more, strict than everyday life. Intuitively science might seem to be a place where we operate under especially strict epistemic standards. And in a sense this is true, so long as one understands the "we" in a genuinely plural sense. That is to say, it is the group that must achieve high standards of rigour, and how individuals contribute to that may be somewhat indirect. For the group to achieve this goal we may well need the individuals who make up the group to be emboldened to take creative leaps and offer bold conjectures on matters that are complex, abstruse, and generally difficult to gain any epistemic purchase on. In this way our study of the somewhat niche topic of norms for avowals connects up with classic themes in the philosophy of science (e.g. Feyerabend, 1993 ; Popper, 2014 ), wherein it is emphasised that for science as a whole to progress individual scientists may operate in a somewhat gung-ho or ad-hoc manner.

We think that what is required here is some form of contextualist epistemic norm (see DeRose, 2002 for a factive version of this purported norm). Context, provided by the history and present consensus in a field, specifies some amount of the previous literature one must survey to check for coherence, and which methodological procedures one must carry out to reach a conclusion that is worth reporting to others. One’s avowal must be such that if one’s total evidence were what one had gathered in the methodologically proper way for one’s latest study, combined with whatever one has taken from the mandated subset of the previous literature contains, then one would be justified in believing one’s scientific public avowal. Public avowals are or should be held to specific subfield-specific norms thus generated—these norms for specifying the requisite literature to survey and adopting methods whose results are worth reporting on are implicitly taught to young scientists as part of their graduate and postdoctoral training.

Such a norm captures the sense in which researchers are held to demanding standards while remaining consistent with the various arguments we have offered showing that apt scientific public avowals need be neither true, known, believed, justified, reasonable to be believed, nor believed to be any such. We note that in the case of philosophy people have also argued that it is permissible to put forward claims even which one does not believe and are not justified (Barnett, 2019 ; Goldberg, 2013 ; Plakias, 2019 ). Hence if something like this norm is found to be operative in scientific fields it may well be a source of similarity between scientific and humanistic research. Coming to self-conscious understanding of such a norm may thus contribute in some small way to bridging the infamous two cultures divide.

The project for future inquiry would be to try and specify the details of what is implicitly being taught as normatively correct for putting forward avowals, and submit it to epistemic appraisal. To some extent, while not done explicitly under this aegis, the response to the replication crisis has led to just this discussion. Renewed consideration of standards of statistical significance and the ways in which journals decide what ought to be published have taken centre stage in scientists' discussions of their own practice. Rendering this more systematic and linking it more explicitly to epistemic theory would not only be interesting itself, but would also speak to the questions of science communication raised earlier. If we are to rely on scientific papers to guide policy and public discussion, it would be useful for us all to be clear on exactly what the epistemic status of the main claims of published scientific papers are. It would be inappropriate for us to rely on inter-scientific testimony as if they were put forward as proper assertions. Rather, we should read them as statements being put forward under very specific social epistemic contexts.

6 Conclusion

When stating their central claims scientists should not be held to the kind of norms we hold assertions to if collective inquiry is to flourish. At the least, properly put forward scientific public avowals frequently do not and need not satisfy those norms of assertion that have been discussed in the analytic epistemology literature. Public avowals in science ought to be governed by a different norm.

We have suggested a contextualist justificatory norm as our proposal. But of course there are other possibilities, and this should be considered an area open for future inquiry. We note one especially interesting idea, drawing on the work of Yablo ( 2014 ). A long tradition sees science as in the business of putting forward partial truths, and our case study could be read that way. One could thus develop an account of the appropriate standards for scientific public avowals based upon a partial factive norm. It would take us very far afield to develop the technical machinery necessary to properly study this, and in any case, we prefer our contextualist justificatory account. However, we raise the possibility here partly to acknowledge it, and partly to make it apparent how much room there is for fruitful development in the theoretical study of what sort of norms should govern particular acts of putting forward conclusions in light of the wider project of collective inquiry.

Underlying all our arguments is the conviction that a scientific research community must ensure its members must spread out across logical space. We must allow for the exploration of different theories, by different methods, and accept that there will be different positions adopted as time goes by and results accumulate. Perhaps inquiry shall prove to be a process of never ending adjustment, and this will be our state in perpetuity. Or perhaps we may eventually learn from science what is actual. But even if so, in order to get there, we must allow that in the midst of inquiry, scientific public avowals will frequently be defences of implausible possibilities.

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Acknowledgments

This paper has been in the works for several years, so many of our colleagues and members of the community have contributed useful comments on various aspects of this work. The authors would also like to thank anonymous reviewers for providing valuable feedback. HD’s research leading to these results has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 818633).

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Dang, H., Bright, L.K. Scientific conclusions need not be accurate, justified, or believed by their authors. Synthese 199 , 8187–8203 (2021). https://doi.org/10.1007/s11229-021-03158-9

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