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Developing Theories & Hypotheses

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2.5: Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.

Theories and hypotheses always have this if-then relationship. “ If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this question is an interesting one on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure \(\PageIndex{1}\) shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

• Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274 ↵
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

2.4 Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.2  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be  logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be  positive.  That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

• A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
• Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
• Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
• Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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Falsifiability

Karl popper's basic scientific principle, karl popper's basic scientific principle.

Falsifiability, according to the philosopher Karl Popper, defines the inherent testability of any scientific hypothesis.

This article is a part of the guide:

• Inductive Reasoning
• Deductive Reasoning
• Hypothetico-Deductive Method
• Scientific Reasoning
• Testability

Browse Full Outline

• 1 Scientific Reasoning
• 2.1 Falsifiability
• 2.2 Verification Error
• 2.3 Testability
• 2.4 Post Hoc Reasoning
• 3 Deductive Reasoning
• 4.1 Raven Paradox
• 5 Causal Reasoning
• 6 Abductive Reasoning
• 7 Defeasible Reasoning

Science and philosophy have always worked together to try to uncover truths about the universe we live in. Indeed, ancient philosophy can be understood as the originator of many of the separate fields of study we have today, including psychology, medicine, law, astronomy, art and even theology.

Scientists design experiments and try to obtain results verifying or disproving a hypothesis, but philosophers are interested in understanding what factors determine the validity of scientific endeavors in the first place.

Whilst most scientists work within established paradigms, philosophers question the paradigms themselves and try to explore our underlying assumptions and definitions behind the logic of how we seek knowledge. Thus there is a feedback relationship between science and philosophy - and sometimes plenty of tension!

One of the tenets behind the scientific method is that any scientific hypothesis and resultant experimental design must be inherently falsifiable. Although falsifiability is not universally accepted, it is still the foundation of the majority of scientific experiments. Most scientists accept and work with this tenet, but it has its roots in philosophy and the deeper questions of truth and our access to it.

What is Falsifiability?

Falsifiability is the assertion that for any hypothesis to have credence, it must be inherently disprovable before it can become accepted as a scientific hypothesis or theory.

For example, someone might claim "the earth is younger than many scientists state, and in fact was created to appear as though it was older through deceptive fossils etc.” This is a claim that is unfalsifiable because it is a theory that can never be shown to be false. If you were to present such a person with fossils, geological data or arguments about the nature of compounds in the ozone, they could refute the argument by saying that your evidence was fabricated to appeared that way, and isn’t valid.

Importantly, falsifiability doesn’t mean that there are currently arguments against a theory, only that it is possible to imagine some kind of argument which would invalidate it. Falsifiability says nothing about an argument's inherent validity or correctness. It is only the minimum trait required of a claim that allows it to be engaged with in a scientific manner – a dividing line between what is considered science and what isn’t. Another important point is that falsifiability is not any claim that has yet to be proven true. After all, a conjecture that hasn’t been proven yet is just a hypothesis.

The idea is that no theory is completely correct , but if it can be shown both to be falsifiable  and supported with evidence that shows it's true, it can be accepted as truth.

For example, Newton's Theory of Gravity was accepted as truth for centuries, because objects do not randomly float away from the earth. It appeared to fit the data obtained by experimentation and research , but was always subject to testing.

However, Einstein's theory makes falsifiable predictions that are different from predictions made by Newton's theory, for example concerning the precession of the orbit of Mercury, and gravitational lensing of light. In non-extreme situations Einstein's and Newton's theories make the same predictions, so they are both correct. But Einstein's theory holds true in a superset of the conditions in which Newton's theory holds, so according to the principle of Occam's Razor , Einstein's theory is preferred. On the other hand, Newtonian calculations are simpler, so Newton's theory is useful for almost any engineering project, including some space projects. But for GPS we need Einstein's theory. Scientists would not have arrived at either of these theories, or a compromise between both of them, without the use of testable, falsifiable experiments. 

Popper saw falsifiability as a black and white definition; that if a theory is falsifiable, it is scientific , and if not, then it is unscientific. Whilst some "pure" sciences do adhere to this strict criterion, many fall somewhere between the two extremes, with  pseudo-sciences  falling at the extreme end of being unfalsifiable. 

Pseudoscience

According to Popper, many branches of applied science, especially social science, are not truly scientific because they have no potential for falsification.

Anthropology and sociology, for example, often use case studies to observe people in their natural environment without actually testing any specific hypotheses or theories.

While such studies and ideas are not falsifiable, most would agree that they are scientific because they significantly advance human knowledge.

Popper had and still has his fair share of critics, and the question of how to demarcate legitimate scientific enquiry can get very convoluted. Some statements are logically falsifiable but not practically falsifiable – consider the famous example of “it will rain at this location in a million years' time.” You could absolutely conceive of a way to test this claim, but carrying it out is a different story.

Thus, falsifiability is not a simple black and white matter. The Raven Paradox shows the inherent danger of relying on falsifiability, because very few scientific experiments can measure all of the data, and necessarily rely upon generalization . Technologies change along with our aims and comprehension of the phenomena we study, and so the falsifiability criterion for good science is subject to shifting.

For many sciences, the idea of falsifiability is a useful tool for generating theories that are testable and realistic. Testability is a crucial starting point around which to design solid experiments that have a chance of telling us something useful about the phenomena in question. If a falsifiable theory is tested and the results are significant , then it can become accepted as a scientific truth.

The advantage of Popper's idea is that such truths can be falsified when more knowledge and resources are available. Even long accepted theories such as Gravity, Relativity and Evolution are increasingly challenged and adapted.

The major disadvantage of falsifiability is that it is very strict in its definitions and does not take into account the contributions of sciences that are observational and descriptive .

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Martyn Shuttleworth , Lyndsay T Wilson (Sep 21, 2008). Falsifiability. Retrieved Apr 15, 2024 from Explorable.com: https://explorable.com/falsifiability

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What does it mean for science to be falsifiable?

Posted on July 31, 2021 by Evan Arnet

Science is falsifiable. Or at least, this is what I (like many Americans) learned in many of my high school and college science classes. Clearly, the idea has appeal among scientists and non-scientists alike:

But what exactly does “falsifiable” mean? And why is it valued by some scientists, but dismissed or even considered actively harmful by others?

Imagine you are an infectious disease expert investigating COVID-19. You want to determine whether, absent vaccination, COVID-19 always causes at least some lung damage. To prove this claim is true, you would have to check every case and see if every time a patient has COVID, there is also lung damage. And for every case you check, there are more new cases to check.

However, to prove this claim is false, you merely need to document a single case in which someone who previously had COVID has no lung damage. This is an extension of the logical point that to prove a general claim, you need to confirm every instance, but to disprove a general claim, you only need a single counterexample. 

The legendary philosopher of science Karl Popper argued that good science is falsifiable, in that it makes precise claims which can be tested and then discarded (falsified) if they don’t hold up under testing. For example, if you find a case of COVID-19 without lung damage, then you falsify the hypothesis that it always causes lung damage. According to Popper, science progresses by making conjectures, subjecting them to rigorous tests, and then discarding those that fail.

He contrasted this with ostensibly unscientific systems, like astrology. Let’s say your horoscope says “something of consequence will happen in your life tomorrow.” Popper argued that a claim like this is so vague, so devoid of clear content, that it can’t be meaningfully falsified and, therefore, isn’t scientific. 

Contemporary scholars who study scientific methodology are often frustrated by the implication that science is logically falsifiable. The problem is that scientists can always make excuses to avoid falsifying a claim. The discovery of Neptune is a famous case. Astronomers had noticed irregularities in the orbit of Uranus. One possibility would be that these irregularities violated the theory currently used to explain planetary motion, called Newtonian mechanics, and that this theory should be rejected. At face value, these observations seemed to falsify Newtonian mechanics. But, no one actually argued for this. Instead, they searched for explanations for the irregularities — including the possibility of another planet. Two astronomers, Urban Leverrier in France and John Couch Adams in England, independently used mathematics to predict the location of this previously unknown planet. Astronomical observations by Johann Gottfried Galle confirmed the existence of a planet and, thus, Neptune was discovered.

Put simply, to test a hypothesis, you have to make a bunch of other assumptions, or auxiliary hypotheses. You have to assume that your instruments are working, that you did the math correctly, that you didn’t miss any relevant causes (like Neptune), etc. When something goes awry, you can then choose whether the real error lies in your main hypothesis or in an auxiliary hypothesis. 

For an illustration of this problem, imagine you are baking lasagna. You Google lasagna recipes, find a recipe that looks good, and get cooking. You take your lasagna out of the oven, take a bite, and…it tastes terrible. Does this mean you can falsify the hypothesis that the lasagna recipe is good? Not necessarily. Maybe you didn’t follow the recipe correctly, or the olive oil was rancid, or any number of problems other than the recipe itself.

Similar to the COVID example above, we can imagine a scientist arguing that because of poor resolution in a CT scan, lung damage was not detected when it did in fact occur. In other words, the presumed false hypothesis is not that COVID always causes lung damage. Instead, what is allegedly false is the assumption, or auxiliary hypothesis, that the CT scan was detailed enough to detect the lung damage.

This general argument against falsification is sometimes attributed to the philosopher W. V. O. Quine in a famous 1951 article, but it was actually a widely-expressed concern, including by Karl Popper himself. However, Popper thought that features necessary for the testing of scientific claims would be accepted as background conditions by the scientific community and, therefore, falsification could proceed. For example, after it is accepted that the oven temperature is correct and the ingredients are in good condition and measured properly, then one can test whether the lasagna recipe is any good.

Regardless, when a scientist touts the falsifiability of science, it is rare that they are a strict devotee of Popper. (He held some unorthodox views, e.g., we can never actually gain confidence in a theory, we can only eliminate alternatives.) Usually they mean that, unlike some other systems, science makes deliberately clear predictions and actively attempts to disprove claims.

One of the amazing things about science is not so much its tight logical structure — the scientific process can actually be quite messy — but rather, that science aims to test claims and consider countermanding evidence. The sociologist of science Robert Merton referred to this as “organized skepticism.” (Incidentally, despite his reputation for prioritizing logical falsification, Karl Popper was attentive to this social aspect of science.)

Falsification as a matter of scientific practice, rather than logic, is especially significant because humans like to be right. We are inclined to seek out evidence which supports rather than challenges our existing opinions, a well-known phenomenon that is often referred to as confirmation bias . Science fights against this cognitive tendency by encouraging individual scientists to think critically about their own work and for the broader community to be skeptical of each other. 

Falsification does not stand alone as the mark of the scientific, and a lot of scientific research aims to confirm claims or to evaluate claims on metrics other than strict truth or falsity. Nonetheless, the willingness and intent to vigorously confront claims with evidence remains a key aspect of the scientific community. This requires attention to the formulation of claims to ensure they are testable. But, even more important is the careful coordination across the scientific community that allows scientific skepticism to lead to productive research.

Edited by Jennifer Sieben and Joe Vuletich

This was a fantastic explanation of a concept that I’ve always had difficulty understanding.

Great article, you really explain it well! I was looking for the line, “science tries to disprove itself by falsification,” and this article was on the list.

At the health sciences center where I worked for 8 years, the idea was widespread that anybody could come up with an explanation or hypothesis for some physiology or biochemical facts, so much so that you couldn’t be bothered if all it did was explain the data. A lecture with a mathematical model involving modeling biochemistry with 100 different equation in a seminar led to the reaction (from me) , how would you know if one or more equation was wrong? Feynman, the skeptical physicist from the Bronx would make a characteristic short reply to a non-falsifiable claim “how would you know?”. The writers above in this thread point out that a community that uses publication of scientific results in the newly public publications of the new scientific societies of the 16nth century that made replication of studies possible and publication is a key factor. I have heard chemists reply disdainfully of the guy whose published synthesis can never be repeated. You may have heard about the humor magazine “journal of irreproducible results”. Doubting your own assumptions maybe 1 per day, is a potentially painful exercise that is at the heart of being a scientist. A person who tends to rote memorization, or good boy behavior may not be a scientists if they do not think in terms of falsification but simply truthiness. It is disturbing that some people propose that string theory does not need to generate testable results and can get by on beauty alone.

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Karl Popper: Theory of Falsification

Saul Mcleod, PhD

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Karl Popper’s theory of falsification contends that scientific inquiry should aim not to verify hypotheses but to rigorously test and identify conditions under which they are false. For a theory to be valid according to falsification, it must produce hypotheses that have the potential to be proven incorrect by observable evidence or experimental results. Unlike verification, falsification focuses on categorically disproving theoretical predictions rather than confirming them.

• Karl Popper believed that scientific knowledge is provisional – the best we can do at the moment.
• Popper is known for his attempt to refute the classical positivist account of the scientific method by replacing induction with the falsification principle.
• The Falsification Principle, proposed by Karl Popper, is a way of demarcating science from non-science. It suggests that for a theory to be considered scientific, it must be able to be tested and conceivably proven false.
• For example, the hypothesis that “all swans are white” can be falsified by observing a black swan.
• For Popper, science should attempt to disprove a theory rather than attempt to continually support theoretical hypotheses.

Theory of Falsification

Karl Popper is prescriptive and describes what science should do (not how it actually behaves). Popper is a rationalist and contended that the central question in the philosophy of science was distinguishing science from non-science.

Karl Popper, in ‘The Logic of Scientific Discovery’ emerged as a major critic of inductivism, which he saw as an essentially old-fashioned strategy.

Popper replaced the classical observationalist-inductivist account of the scientific method with falsification (i.e., deductive logic) as the criterion for distinguishing scientific theory from non-science.

All inductive evidence is limited: we do not observe the universe at all times and in all places. We are not justified, therefore, in making a general rule from this observation of particulars.

According to Popper, scientific theory should make predictions that can be tested, and the theory should be rejected if these predictions are shown not to be correct.

He argued that science would best progress using deductive reasoning as its primary emphasis, known as critical rationalism.

Popper gives the following example:

Europeans, for thousands of years had observed millions of white swans. Using inductive evidence, we could come up with the theory that all swans are white.

However, exploration of Australasia introduced Europeans to black swans.  Poppers’ point is this: no matter how many observations are made which confirm a theory, there is always the possibility that a future observation could refute it.  Induction cannot yield certainty.

Karl Popper was also critical of the naive empiricist view that we objectively observe the world. Popper argued that all observation is from a point of view, and indeed that all observation is colored by our understanding. The world appears to us in the context of theories we already hold: it is ‘theory-laden.’

Popper proposed an alternative scientific method based on falsification.  However, many confirming instances exist for a theory; it only takes one counter-observation to falsify it. Science progresses when a theory is shown to be wrong and a new theory is introduced that better explains the phenomena.

For Popper, the scientist should attempt to disprove his/her theory rather than attempt to prove it continually. Popper does think that science can help us progressively approach the truth, but we can never be certain that we have the final explanation.

Critical Evaluation

Popper’s first major contribution to philosophy was his novel solution to the problem of the demarcation of science. According to the time-honored view, science, properly so-called, is distinguished by its inductive method – by its characteristic use of observation and experiment, as opposed to purely logical analysis, to establish its results.

The great difficulty was that no run of favorable observational data, however long and unbroken, is logically sufficient to establish the truth of an unrestricted generalization.

Popper’s astute formulations of logical procedure helped to reign in the excessive use of inductive speculation upon inductive speculation, and also helped to strengthen the conceptual foundation for today’s peer review procedures.

However, the history of science gives little indication of having followed anything like a methodological falsificationist approach.

Indeed, and as many studies have shown, scientists of the past (and still today) tended to be reluctant to give up theories that we would have to call falsified in the methodological sense, and very often, it turned out that they were correct to do so (seen from our later perspective).

The history of science shows that sometimes it is best to ’stick to one’s guns’. For example, “In the early years of its life, Newton’s gravitational theory was falsified by observations of the moon’s orbit”

Also, one observation does not falsify a theory. The experiment may have been badly designed; data could be incorrect.

Quine states that a theory is not a single statement; it is a complex network (a collection of statements). You might falsify one statement (e.g., all swans are white) in the network, but this should not mean you should reject the whole complex theory.

Critics of Karl Popper, chiefly Thomas Kuhn , Paul Feyerabend, and Imre Lakatos, rejected the idea that there exists a single method that applies to all science and could account for its progress.

Popperp, K. R. (1959). The logic of scientific discovery . University Press.

Further Information

• Thomas Kuhn – Paradigm Shift Is Psychology a Science?
• Steps of the Scientific Method
• Positivism in Sociology: Definition, Theory & Examples
• The Scientific Revolutions of Thomas Kuhn: Paradigm Shifts Explained

Karl Popper pp 23–54 Cite as

The Discovery of the Falsifiability Principle

• Friedel Weinert 2  
• First Online: 01 January 2023

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Part of the book series: Springer Biographies ((SPRINGERBIOGS))

Popper is most famous for his principle of falsifiability . It is striking that, throughout his career, he used three terms synonymously: falsifiability , refutability and testability . In order to appreciate the importance of these criteria it is helpful to understand how he arrived at these notions, whether they can be used interchangeably and whether scientists find this terminology helpful.

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In a letter (30/11/32) to the publisher Paul Buske, Popper mentioned that J. Kraft had proposed two alternative titles: either ‘The Philosophical Preconditions of Natural Science’ or ‘The Problem of Natural Laws’ [Hansen 3.2; my translation]. Buske was one of the publishers on whom Popper pinned his hopes. Hacohen (2000): Chap. 6 provides a detailed account of the tortuous path of Popper’s manuscript to its publication as Logik der Forschung . See also Autobiography (1974): 67.

Gomperz realized that Popper’s book criticized the Vienna Circle, as he wrote to Popper (27/12/32). In a reference letter (21/12/32) to the publisher Paul Siebeck (of J. C. B. Mohr), Gomperz praised Popper’s book for propounding, in clear language, a ‘methodology of scientific knowledge’, which remained close to the ‘procedure of the mathematical natural sciences’ and differed essentially from that of the Vienna Circle [Hansen 3.2; my translation].

Walter Schiff, Popper’s maternal uncle, taught economics and statistics at the University of Vienna.

Schlick was murdered by a former student on 22 June, 1936, as he was leaving the university. In an undated handwritten note ‘In Honour of Moritz Schlick’ Popper conveyed the general impression at the time that he had been murdered by a Nazi [252.01], which is probably true.

In 1977, Stachel became the first editor of the Einstein Papers Project, then based at Boston University.

See, for instance, his Outline of Psychoanalysis (1938) and my discussion in Copernicus , Darwin and Freud (2009: Chap. 3).

The others were the perihelion advance of Mercury and the redshift of light in gravitational fields. In 1964, Irwin I. Shapiro proposed a fourth classic test: the time delay of electromagnetic radiation (such as radar signals) passing the Sun. Gravitational fields also have an effect on the ticking of clocks: a clock in a weak gravitational field runs faster than a clock in a strong gravitational field. In recent years, satellite-based tests have ‘confirmed’ (or in Popper’s terminology, ‘corroborated’) the results of the classic tests.

This logical rule states that if in a conditional sentence: ‘If p, then q’, the consequent q does not hold, then the antecedent p must be negated. So we infer from non-q to non-p. If p stands for a theory and q stands for, say, a prediction, then the falsity of the prediction implies the falsity of the theory.

See Logic 1980: §§3, 22; Realism/Aim 1985: xxii; Alles Leben 1996: 26; All Life 1999: 10; cf. Corvi 1997: Pt. II. In the Introduction to Grundprobleme (1979: XXXVI, 2009: XXXV; cf. C&R 1963: 228) Popper rejected the term ‘falsificationism’ because it conflated ‘falsification’ and ‘falsifiabiliy’. He preferred the term ‘fallibilism’.

Popper dealt with such a situation in an article in Nature (1940). He discusses three interpretations of nebular red shifts: ‘The three theories are logically equivalent, and therefore do not describe alternative facts , but the same facts in alternative languages .’ (‘Interpretation’ 1940: 69–70; italics in original) (He would write further articles in Nature on the arrow of time in the 1950s and 1960s.)

See K. Popper, ‘On theories as nets’, New Scientist (1982, 319–320). Popper repeatedly used this image of theories as nets, starting in Grundprobleme (1979: 487, 2009: 492). ‘We try to examine the world exhaustively by our nets; but its mesh will always let some small fish escape: there will always be enough play for indeterminism.’ (Popper, Open Universe 1982: 47)

Popper’s concern with probability in Logik later led to his well-known propensity interpretation of probability.

This is not just an issue of terminology. The German sociologist Ulrich Beck uses Popper’s criterion of ‘practical fallibilism’ as an element in his theory of the ‘risk society’, because it undermines the traditional image of science, which Popper himself rejected. (Beck 1992: Pt. III, Chap. 7)

On the question of proliferation of hypotheses, David Miller told me that ‘he (Popper) had learnt from his geologist colleague Bob Allan in NZ about Chamberlin's paper ‘The Method of Multiple Working Hypotheses’, which was published in the Journal of Geology ( 5 1897: 837–48, and reprinted in Science in 1965 http://science.sciencemag.org/content/148/3671/754 ). Jeremy Shearmur procured him a copy [349.13].

I understand the difference between alternative and rival theories as that between alternative versions of the same theory, which agree on first principles, and conflicting theories, which disagree on first principles.

Popper frequently stressed the importance of a dogmatic phase, not only in his publications— Autobiography 1974: §§10, 16; ‘Replies’ 1974: 984; Myth 1994: 16; Alles Leben 1996: 121; All Life 1999: 41; Realism/Aim 1983/1985: Introduction 1982: xxii—but also in his correspondence. In a letter to the American physicist and philosopher Abner Shimony (01/02/70), whom he met at Brandeis, he emphasized that, against the slogan of verification, he had to stress the ‘virtues of testing’. He added that “dogmatic thinking” and the defence of a theory against criticism are needed, if we wish to come to a sound appreciation of the value of a theory: if we give in too easily, we shall never find out what is the strength of the theory, and what deserves preservation’. Not happy with Popper’s version of fallibilism, Shimony hoped to persuade him of the power of scientific inference [350.07].

Some of the leading proponents of string theory also embrace the Anthropic Principle. (Susskind 2006: 197) It does not just claim that the world is the way it is because we are here. No, the Anthropic Principle serves to explain the fine-tuning of the constants of nature, without which (intelligent) life would be impossible.

Joseph J. Thomson proposed the ‘plum-pudding’ model in 1904, after his discovery of the electron (1897). The negatively charged electrons were embedded in a positively charged volume, but there was no nucleus. It was replaced by Rutherford’s nucleus model. For more on these models see my book The Scientist as Philosopher (2004) and my articles ‘The Structure of Atom Models’ (2000) and ‘The Role of Probability Arguments in the History of Science’ (2010).

Bondi is famous for his contribution to cosmology. He rejected the Big Bang theory and proposed, in cooperation with Fred Hoyle and Thomas Gold, the alternative steady-state model. Fred Hoyle’s biographer Simon Mitton, of Cambridge University, told me in a private email (06/03/2020) that Hoyle never mentioned Popper. Popper dismissed the Big Bang theory as ‘unimportant’ ( Offene Gesellschaft 1986: 48–50), even as ‘metaphysical’. ( Zukunft 4 1990: 69–70)

For instance the great American physicist Richard Feynman who held that science is not certain, that it starts with ‘guesses’ whose consequences must be compared to experience.

In our conversation at the LSE John Worrall sounded a note of caution with reference to Peter Medawar and Paul Nurse: ‘well, quite honestly, I don’t know whether you really need to read Popper to know pretty soon when you are doing your scientific work that you are not inductively generalizing data, that you do make hypotheses, that you do need to check that these hypotheses are true or not’. But he agreed that ‘far and away more than any other philosopher he does seem to have been generally influential. And generally regarded as a significant figure, more outside the field than within the field, I think’.

Equate Newton’s second law of motion and his law of gravitation: mg = \(G\frac{m{M}_{E}}{{r}^{2}}\) and solve for M E . Here g is the acceleration near the surface of the earth, r is the radius between the centres of the two bodies and G is the gravitational constant.

Winzer (2019); cf. Kneale’s example of Anderson’s discovery of the positron. Kneale (1974: 206–208). Settle (1974: 701–702) discusses some further examples of ‘non-Popperian’ progress in science.

Note that national or racial prejudices are based on inductive steps: from our experience with some people of a nation or a race to all people of that nation or race.

Note that Newton’s theory does not require that all planets rotate from west to east. In our solar system both Venus and Pluto spin from east to west. So, the east-bound spin of most planets in the solar system could not be a universal, all-inclusive law.

According to Hacohen (2000: 133–134, 144), he accepted the method of induction in his psychological work until 1929. As he wrote to John Stachel it was not until then that he realized the close link between induction and demarcation.

John Norton, of the University of Pittsburgh, has recently proposed a richly illustrated material theory of induction, according to which inductive inferences (both enumerative and eliminative) are legitimate as long as they occur on a ‘case-by-case’ basis. Norton (2021: v–viii; 4–8) claims that ‘all induction is local’ and that ‘no universal rules of induction’ exist. Particular inferences are warranted by ‘background facts in some domain’ which ‘tell us what are good and bad inductive inferences in that domain’.

Several articles in O’Hear ed. (1995), for instance by Newton-Smith and Lipton, elaborate on these inductive elements. There are, therefore, in Popper’s account inductive assumptions. One of the authors who pointed out that ‘falsificationism’ requires inductive assumptions, was my former colleague Anthony O’Hear (1980). Popper complained to him that he did not like his book, (although he admits that his own account contains a ‘whiff of verificationism’). Anthony told me in an email (28/06/20): ‘He (Popper) added that I was “product of the modern education”—by which he meant that I was a follower of Moore and Wittgenstein. But perhaps things were not quite as abrasive as it might have appeared at the time (1980). I found out a lot later that he had told a friend of mine that he (the friend) ought to read my book. He (Popper) did not like it, but it was a serious book, or words to that effect’. Miller (1994: Chap. 2) lists a number of such inductive elements and attempts to eliminate them from Popper’s account.

In his work on political philosophy he condemned the dogmatism, which he detected at work in Plato, Hegel and Marx.

Popper was prone to exaggerations: induction does not exist, a large part of the knowledge of organisms is inborn, all tests boil down to attempted falsifications or everything is a propensity.

In his later work he regarded the notion of verisimilitude (or truthlikeness ) as a more realistic aim of science. ( Objective Knowledge 1972: 57–58) In a panel discussion in the 1980s, he rejected the view, attributed to him, that ‘theories are never true’. ‘This is nonsense. Scientific theories are the ones, which have survived the elimination process’ ( Zukunft 4 1990: 101; my translation).

The theories themselves may be generated from conjectures, intuition or inductive generalization.

Now Appendix *ix of his Logic of Scientific Discovery. Popper ( Myth 1994: 86–87) acknowledges that Bacon was aware of the defect of simple induction by enumeration.

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September 7, 2020

The Idea That a Scientific Theory Can Be ‘Falsified’ Is a Myth

It’s time we abandoned the notion

By Mano Singham

Transit of Mercury across the Sun; Newton's theory of gravity was considered to be "falsified" when it failed to account for the precession of the planet's orbit.

Getty Images

J.B.S. Haldane, one of the founders of modern evolutionary biology theory, was reportedly asked what it would take for him to lose faith in the theory of evolution and is said to have replied, “Fossil rabbits in the Precambrian.” Since the so-called “Cambrian explosion” of 500 million years ago marks the earliest appearance in the fossil record of complex animals, finding mammal fossils that predate them would falsify the theory.

But would it really?

The Haldane story, though apocryphal, is one of many in the scientific folklore that suggest that falsification is the defining characteristic of science. As expressed by astrophysicist Mario Livio in his book Brilliant Blunders : "[E]ver since the seminal work of philosopher of science Karl Popper, for a scientific theory to be worthy of its name, it has to be falsifiable by experiments or observations. This requirement has become the foundation of the ‘scientific method.’”

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But the field known as science studies (comprising the history, philosophy and sociology of science) has shown that falsification cannot work even in principle. This is because an experimental result is not a simple fact obtained directly from nature. Identifying and dating Haldane's bone involves using many other theories from diverse fields, including physics, chemistry and geology. Similarly, a theoretical prediction is never the product of a single theory but also requires using many other theories. When a “theoretical” prediction disagrees with “experimental” data, what this tells us is that that there is a disagreement between two sets of theories, so we cannot say that any particular theory is falsified.

Fortunately, falsification—or any other philosophy of science—is not necessary for the actual practice of science. The physicist Paul Dirac was right when he said , "Philosophy will never lead to important discoveries. It is just a way of talking about discoveries which have already been made.” Actual scientific history reveals that scientists break all the rules all the time, including falsification. As philosopher of science Thomas Kuhn noted, Newton's laws were retained despite the fact that they were contradicted for decades by the motions of the perihelion of Mercury and the perigee of the moon. It is the single-minded focus on finding what works that gives science its strength, not any philosophy. Albert Einstein said that scientists are not, and should not be, driven by any single perspective but should be willing to go wherever experiment dictates and adopt whatever works .

Unfortunately, some scientists have disparaged the entire field of science studies, claiming that it was undermining public confidence in science by denying that scientific theories were objectively true. This is a mistake since science studies play vital roles in two areas. The first is that it gives scientists a much richer understanding of their discipline. As Einstein said : "So many people today—and even professional scientists—seem to me like somebody who has seen thousands of trees but has never seen a forest. A knowledge of the historic and philosophical background gives that kind of independence from prejudices of his generation from which most scientists are suffering. This independence created by philosophical insight is—in my opinion—the mark of distinction between a mere artisan or specialist and a real seeker after truth." The actual story of how science evolves results in inspiring more confidence in science, not less.

The second is that this knowledge equips people to better argue against antiscience forces that use the same strategy over and over again, whether it is about the dangers of tobacco, climate change, vaccinations or evolution. Their goal is to exploit the slivers of doubt and discrepant results that always exist in science in order to challenge the consensus views of scientific experts. They fund and report their own results that go counter to the scientific consensus in this or that narrow area and then argue that they have falsified the consensus. In their book Merchants of Doubt, historians Naomi Oreskes and Erik M. Conway say that for these groups “[t]he goal was to fight science with science—or at least with the gaps and uncertainties in existing science, and with scientific research that could be used to deflect attention from the main event.”

Science studies provide supporters of science with better arguments to combat these critics, by showing that the strength of scientific conclusions arises because credible experts use comprehensive bodies of evidence to arrive at consensus judgments about whether a theory should be retained or rejected in favor of a new one. These consensus judgments are what have enabled the astounding levels of success that have revolutionized our lives for the better. It is the preponderance of evidence that is relevant in making such judgments, not one or even a few results.

So, when anti-vaxxers or anti-evolutionists or climate change deniers point to this or that result to argue that they have falsified the scientific consensus, they are making a meaningless statement. What they need to do is produce a preponderance of evidence in support of their case, and they have not done so.

Falsification is appealing because it tells a simple and optimistic story of scientific progress, that by steadily eliminating false theories we can eventually arrive at true ones. As Sherlock Holmes put it, “When you have eliminated the impossible, whatever remains, however improbable, must be the truth.” Such simple but incorrect narratives abound in science folklore and textbooks. Richard Feynman in his book QED , right after “explaining” how the theory of quantum electrodynamics came about, said, "What I have just outlined is what I call a “physicist’s history of physics,” which is never correct. What I am telling you is a sort of conventionalized myth-story that the physicists tell to their students, and those students tell to their students, and is not necessarily related to the actual historical development which I do not really know!"

But if you propagate a “myth-story” enough times and it gets passed on from generation to generation, it can congeal into a fact, and falsification is one such myth-story.

It is time we abandoned it.

Scientific Method: What it is, How to Use It: Scientific Method

• Scientific Method
• Step 1: Question
• Step 2: Research
• Step 3: Hypothesis
• Step 4: Experiment
• Step 5: Data
• Step 6: Conclusion

What is the Scientific Method?

The scientific method  is a standardized way of making observations, gathering data, forming theories, testing predictions, and interpreting results.   Does this mean all scientists follow this  exact  process? No. Some areas of science can be more easily tested than others.

For example, scientists studying how stars change as they age or how dinosaurs digested their food cannot fast-forward a star's life by a million years or run medical exams on feeding dinosaurs to test their hypotheses. When direct experimentation is not possible, scientists modify the scientific method. In fact, there are probably as many versions of the scientific method as there are scientists!

But even when modified, the goal remains the same:  to discover cause and effect relationships by asking questions, carefully gathering and examining the evidence, and seeing if all the available information can be combined in to a logical answer.

The Four Factors of Conducting Good Scientific Research

• Replication
• Falsifiable
• Parsimonious

1. Research must be  Replicable,  meaning that other researchers must be able to repeat the study and get the same results. This is why in a scientific study, researchers take the time not only to describe their results but also the methods they used to achieve their results. 

As scientists do their research and make sure that it's replicable, they'll develop a theory and translate that theory into a hypothesis.  A  Hypothesis  is a testable prediction of what will happen given a certain set of conditions. A good theory must do two things: organize many observations in a logical way and allow researchers to come up with clear predictions to check the theory.

A good theory or hypothesis also must be  Falsifiable , which means that it must be stated in a way that makes it possible to reject it. In other words, we have to be able to prove a theory or hypothesis wrong.

Theories and hypotheses need to be falsifiable because otherwise research will present confirmation bias. Researchers who display  Confirmation Bias  look for and accept evidence that supports what they want to believe and ignore or reject evidence that refutes their beliefs.

Falsifiability doesn’t mean that there are currently arguments against a theory, only that it is possible to imagine some kind of argument which would invalidate it. Falsifiability says nothing about an argument's inherent validity or correctness. It is only the basic requirement of a theory which allows it to be considered scientific. An important note however, is that falsifiability is not simply any claim that has yet to be proven true. 

• Does Science Need Falsifiability? An article by Kate Becker in PBS's Nova explains the value and necessity of making scientific research falsifiable.

By stating hypotheses precisely, scientists ensure that they can replicate their own and others’ research. To make hypotheses more precise, researchers use operational definitions to define the variables they study.  Operational Definitions  state exactly how a variable will be measured.

Precision and accuracy are two ways that scientists think about error.  Accuracy  refers to how close a measurement is to the true or accepted value. Precision refers to how close measurements of the same item are to each other. Precision  is independent of accuracy which means it is possible to be very precise but not very accurate , and it is also possible to be accurate without being precise. The best quality scientific observations are both accurate and precise.

The easiest way to illustrate the difference between precision and accuracy is with the analogy of a dartboard. 

• In example A, the darts are neither close to the bulls-eye, nor close to each other, meaning there is neither accuracy, nor precision. 
• In example B, all of the darts land very close together, but far from the bulls-eye. There is precision, but not accuracy  
• In example C, the darts are all about an equal distance from and spaced equally around the bulls-eye there is accuracy because the average of the darts would be in the bulls-eye. This represents data that is accurate, but not precise. 
• In example D, the darts land close to the bulls-eye and close together.  Meaning there is both accuracy and precision.

Parsimonious  means “being thrifty or stingy.” A person who values parsimony will apply the thriftiest or most logically economical explanation for a set of phenomena.

The  Principle Of Parsimony , also called  Occam’s Razor , maintains that researchers should apply the simplest explanation possible to any set of observations. For instance, scientists try to explain results by using well-accepted theories instead of elaborate new hypotheses. Parsimony prevents researchers from inventing and pursuing outlandish theories.

What Parsimony means in practice is we should go with the weight of the evidence available to us. This will probably seem very obvious, but in practice it is essential that we have a philosophically justified method of choosing between explanations of our data. After all, when there is good evidence to support one idea and only slightly less good evidence to support another – can you really chose between them? Well, yes. You *MUST* take number 1.

Scientific Method (Infographic)

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Law of Falsifiability

The Law of Falsifiability is a rule that a famous thinker named Karl Popper came up with. In simple terms, for something to be called scientific, there must be a way to show it could be incorrect. Imagine you’re saying you have an invisible, noiseless, pet dragon in your room that no one can touch or see. If no one can test to see if the dragon is really there, then it’s not scientific. But if you claim that water boils at 100 degrees Celsius at sea level, we can test this. If it turns out water does not boil at this temperature under these conditions, then the claim would be proven false. That’s what Karl Popper was getting at – science is about making claims that can be tested, possibly shown to be false, and that’s what keeps it trustworthy and moving forward.

Examples of Law of Falsifiability

• Astrology – Astrology is like saying certain traits or events will happen to you based on star patterns. But because its predictions are too general and can’t be checked in a clear way, it doesn’t pass the test of falsifiability. This means astrology cannot be considered a scientific theory since you can’t show when it’s wrong with specific tests.
• The Theory of Evolution – In contrast, the theory of evolution is something we can test. It says that different living things developed over a very long time. If someone were to find an animal’s remains in a rock layer where it should not be, such as a rabbit in rock that’s 500 million years old, that would challenge the theory. Since we can test it by looking for evidence like this, evolution is considered falsifiable.

Why is it Important?

The Law of Falsifiability matters a lot because it separates what’s considered scientific from what’s not. When an idea can’t be tested or shown to be wrong, it can lead people down the wrong path. By focusing on theories we can test, science gets stronger and we learn more about the world for real. For everyday people, this is key because it means we can rely on science for things like medicine, technology, and understanding our environment. If scientists didn’t use this rule, we might believe in things that aren’t true, like magic potions or the idea that some stars can predict your future.

Implications and Applications

The rule of being able to test if something is false is basic in the world of science and is used in all sorts of subjects. For example, in an experiment, scientists try really hard to see if their guess about something can be shown wrong. If their guess survives all the tests, it’s a good sign; if not, they need to think again or throw it out. This is how science gets better and better.

Comparison with Related Axioms

• Verifiability : This means checking if a statement or idea is true. Both verifiability and falsifiability have to do with testing, but falsifiability is seen as more important because things that can be proven wrong are usually also things we can check for truth.
• Empiricism : This is the belief that knowledge comes from what we can sense – like seeing, hearing, or touching. Falsifiability and empiricism go hand in hand because both involve using real evidence to test out ideas.
• Reproducibility : This idea says that doing the same experiment in the same way should give you the same result. To show something is falsifiable, you should be able to repeat a test over and over, with the chance that it might fail.

Karl Popper brought the Law of Falsifiability into the world in the 1900s. He didn’t like theories that seemed to answer everything because, to him, they actually explained nothing. By making this law, he aimed to make a clear line between what could be taken seriously in science and what could not. It was his way of making sure scientific thinking stayed sharp and clear.

Controversies

Not everyone agrees that falsifiability is the only way to tell if something is scientific. Some experts point out areas in science, like string theory from physics, which are really hard to test and so are hard to apply this law to. Also, in science fields that look at history, like how the universe began or how life changed over time, it’s not always about predictions that can be tested, but more about understanding special events. These differences in opinion show that while it’s a strong part of scientific thinking, falsifiability might not work for every situation or be the only thing that counts for scientific ideas.

Related Topics

• Scientific Method : This is the process scientists use to study things. It involves asking questions, making a hypothesis, running experiments, and seeing if the results support the hypothesis. Falsifiability is part of this process because scientists have to be able to test their hypotheses.
• Peer Review : When scientists finish their work, other experts check it to make sure it was done right. This involves reviewing if the experiments and tests were set up in a way that they could have shown the work was false if it wasn’t true.
• Logic and Critical Thinking : These are skills that help us make good arguments and decisions. Understanding falsifiability helps people develop these skills because it teaches them to always look for ways to test ideas.

In conclusion, the Law of Falsifiability, as brought up by Karl Popper, is like a key part of a scientist’s toolbox. It makes sure that ideas need to be able to be tested and possibly shown to be not true. By using this rule, we avoid believing in things without good evidence, and we make the stuff we learn about the world through science stronger and more reliable.

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What Is a Falsifiable Hypothesis?

A falsifiable hypothesis is a proposed explanation for an event or occurrence that can be proven false. The falsifiability of a hypothesis requires that the statement can be refuted based on a scientific and observable investigation.

The quality of a hypothesis subject to falsification is an essential part of any scientific experiment. Prior to proving a scientific theory, a hypothesis must be formulated. There are many forms of hypotheses, and tests may be conducted to determine if the hypothesis is right or wrong. Scientific standards require that the hypothesis must be not only testable but also falsifiable.

An example of a hypothesis that is not falsifiable is an educated guess that there are no other human life forms in the universe apart from those on Earth. This hypothesis can be tested through several methods to prove that the statement is true. One proof that the hypothesis is true is when a team of astronauts or a remotely operated probe sent to space found life forms in the galaxy. Another proof is if radio signals sent to outer space will be returned to Earth by aliens, or if these aliens land on the planet to make contact with human beings. However, there is no absolute way to determine that the hypothesis is false; there is no test to prove that life forms don’t exist outside of Earth.

A good example of a falsifiable hypothesis is the statement that all swans are white. Although most swans are white in color, finding just one swan that has black feathers will prove the hypothesis false.

In scientific experiments, it is not important that the hypothesis cannot be proven true. What is more essential is that the hypothesis can be tested and proven false.

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