what does falsifiable hypothesis mean

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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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 25, 2024 from Explorable.com: https://explorable.com/falsifiability

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The Discovery of the Falsifiability Principle

• First Online: 01 January 2023

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• Friedel Weinert 2  

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

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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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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.

What is a scientific hypothesis?

It's the initial building block in the scientific method.

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

• If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
• If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
• If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

• Read more about writing a hypothesis, from the American Medical Writers Association.
• Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
• Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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Karl Popper: Theory of Falsification

Saul Mcleod, PhD

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BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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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.

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

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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5 Falsifiability

Textbook chapters (or similar texts).

• Deductive Logic
• Persuasive Reasoning and Fallacies
• The Falsifiability Criterion of Science
• Understanding Science

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• Why a Confirmation Strategy Dominates Psychological Science

*******************************************************

Inquiry-based Activity:  Popular media and falsifiability

Introduction : Falsifiability, or the ability for a statement/theory to be shown to be false, was noted by Karl Popper to be the clearest way to distinguish science from pseudoscience. While incredibly important to scientific inquiry, it is also important for students to understand how this criterion can be applied to the news and information they interact with in their day-to-day lives. In this activity, students will apply the logic of falsifiability to rumors and news they have heard of in the popular media, demonstrating the applicability of scientific thinking to the world beyond the classroom.

Question to pose to students : Think about the latest celebrity rumor you have heard about in the news or through social media. If you cannot think of one, some examples might include, “the CIA killed Marilyn Monroe” and “Tupac is alive.” Have students get into groups, discuss their rumors, and select one to work with.

Note to instructors: Please modify/update these examples if needed to work for the students in your course. Snopes is a good source for recent examples.

Students form a hypothesis : Thinking about that rumor, decide what evidence would be necessary to prove that it was correct. That is, imagine you were a skeptic and automatically did not believe the rumor – what would someone need to tell or show you to convince you that it was true?

Students test their hypotheses : Each group (A) should then pair up with one other group (B) and try to convince them their rumor is true, providing them with the evidence from above. Members of group B should then come up with any reasons they can think of why the rumor may still be false. For example – if “Tupac is alive” is the rumor and “show the death certificate” is a piece of evidence provided by group A, group B could posit that the death certificate was forged by whoever kidnapped Tupac. Once group B has evaluated all of group A’s evidence, have the groups switch such that group B is now trying to convince group A about their rumor.

Do the students’ hypotheses hold up? : Together, have the groups work out whether the rumors they discussed are falsifiable. That is, can it be “proven?” Remember, a claim is non-falsifiable if there can always be an explanation for the absence of evidence and/or an exhaustive search for evidence would be required. Depending on the length of your class, students can repeat the previous step with multiple groups.

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Meaning of falsifiable in English

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• Propositions which are presented to be true can be used in support of other propositions that are presumed to be falsifiable.
• Their aim is to provide easily falsifiable hypotheses .
• It's a falsifiable theory .
• bluff someone into something/doing something
• false modesty
• pass something off as something
• phony-baloney

Examples of falsifiable

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relating to the scientific study of animals, especially their structure

Dead ringers and peas in pods (Talking about similarities, Part 2)

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What Is a Testable Hypothesis?

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A hypothesis is a tentative answer to a scientific question. A testable hypothesis is a  hypothesis that can be proved or disproved as a result of testing, data collection, or experience. Only testable hypotheses can be used to conceive and perform an experiment using the scientific method .

Requirements for a Testable Hypothesis

In order to be considered testable, two criteria must be met:

• It must be possible to prove that the hypothesis is true.
• It must be possible to prove that the hypothesis is false.
• It must be possible to reproduce the results of the hypothesis.

Examples of a Testable Hypothesis

All the following hypotheses are testable. It's important, however, to note that while it's possible to say that the hypothesis is correct, much more research would be required to answer the question " why is this hypothesis correct?" 

• Students who attend class have higher grades than students who skip class.  This is testable because it is possible to compare the grades of students who do and do not skip class and then analyze the resulting data. Another person could conduct the same research and come up with the same results.
• People exposed to high levels of ultraviolet light have a higher incidence of cancer than the norm.  This is testable because it is possible to find a group of people who have been exposed to high levels of ultraviolet light and compare their cancer rates to the average.
• If you put people in a dark room, then they will be unable to tell when an infrared light turns on.  This hypothesis is testable because it is possible to put a group of people into a dark room, turn on an infrared light, and ask the people in the room whether or not an infrared light has been turned on.

Examples of a Hypothesis Not Written in a Testable Form

• It doesn't matter whether or not you skip class.  This hypothesis can't be tested because it doesn't make any actual claim regarding the outcome of skipping class. "It doesn't matter" doesn't have any specific meaning, so it can't be tested.
• Ultraviolet light could cause cancer.  The word "could" makes a hypothesis extremely difficult to test because it is very vague. There "could," for example, be UFOs watching us at every moment, even though it's impossible to prove that they are there!
• Goldfish make better pets than guinea pigs.  This is not a hypothesis; it's a matter of opinion. There is no agreed-upon definition of what a "better" pet is, so while it is possible to argue the point, there is no way to prove it.

How to Propose a Testable Hypothesis

Now that you know what a testable hypothesis is, here are tips for proposing one.

• Try to write the hypothesis as an if-then statement. If you take an action, then a certain outcome is expected.
• Identify the independent and dependent variable in the hypothesis. The independent variable is what you are controlling or changing. You measure the effect this has on the dependent variable.
• Write the hypothesis in such a way that you can prove or disprove it. For example, a person has skin cancer, you can't prove they got it from being out in the sun. However, you can demonstrate a relationship between exposure to ultraviolet light and increased risk of skin cancer.
• Make sure you are proposing a hypothesis you can test with reproducible results. If your face breaks out, you can't prove the breakout was caused by the french fries you had for dinner last night. However, you can measure whether or not eating french fries is associated with breaking out. It's a matter of gathering enough data to be able to reproduce results and draw a conclusion.
• What Are Examples of a Hypothesis?
• What Are the Elements of a Good Hypothesis?
• What Is a Hypothesis? (Science)
• Understanding Simple vs Controlled Experiments
• How To Design a Science Fair Experiment
• Scientific Method Vocabulary Terms
• Null Hypothesis Definition and Examples
• Theory Definition in Science
• Hypothesis, Model, Theory, and Law
• Six Steps of the Scientific Method
• What 'Fail to Reject' Means in a Hypothesis Test
• Scientific Method Flow Chart
• Null Hypothesis Examples
• Definition of a Hypothesis
• What Is an Experiment? Definition and Design

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falsifiable

[ fawl-s uh - fahy - uh -b uh l ]

Using this technology ensures that customer transactions are tamper-resistant and not falsifiable.

All scientific theories are falsifiable: if evidence that contradicts a theory comes to light, the theory itself is either modified or discarded.

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Other words from.

• fal·si·fi·a·bil·i·ty [ fawl-s, uh, -fahy-, uh, -, bil, -i-tee ] noun
• non·fal·si·fi·a·ble adjective
• un·fal·si·fi·a·ble adjective

Word History and Origins

Origin of falsifiable 1

Example Sentences

The problem is that slavishly performing this procedure absolves researchers of having to develop theories that make specific, falsifiable predictions — the fundamental elements of good science.

Perhaps the worst way to combat what QAnon adherents say is to treat it as something falsifiable.

[ ak -s uh -lot-l ]

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