an example of a falsifiable hypothesis

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

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Examples of Falsifiability

I came across the notion of falsifiability quite recently. The wikipedia article on the same states that:

Falsifiability or refutability of a statement, hypothesis, or theory is the inherent possibility that it can be proven false. A statement is called falsifiable if it is possible to conceive of an observation or an argument which negates the statement in question. In this sense, falsify is synonymous with nullify, meaning to invalidate or "show to be false". For a statement to be questioned using observation, it needs to be at least theoretically possible that it can come into conflict with observation.

While I can understand the general concept - I would like to have a deeper understanding of the same. Popper mentions that this notion differentiates science from pseudo - science.

Can someone please give me some examples for the same? - So that I might understand the idea more intuitively. Specifically if you could provide what would be the falsifiability arguments/observations would be for:

Newton's theory of gravitation.

Heliocentralism

Theorem of calculus.

Probability theory.

Basically two popular theories from the realm of physics and two popular theories from mathematics (which I might possibly be familiar with), would do. Need not be just these four.

• philosophy-of-science
• falsifiability

• Newton's theory: a free apple "falling" from the floor to the ceiling. –  Mauro ALLEGRANZA Commented Jul 2, 2016 at 15:29
• Right - if we observe that theory of gravitation would be falsified. –  user12196 Commented Jul 2, 2016 at 15:43
• Of course, the more complex is the theory, more difficult is to found "simple" falsifying conditions like that. When many "factors" are involved, a falsifying experiment must "manage" all of them. Consider the well-known discovery of Neptune by Urbain Le Verrier : a potential "falsifier" has been transformed into a brilliant "verification". –  Mauro ALLEGRANZA Commented Jul 2, 2016 at 15:56
• 2 For mathematical theories, it is not so clear if Popper's criteria applies. In principle, we can say that the only way to "falsify" a math theory is proving his inconsistency. –  Mauro ALLEGRANZA Commented Jul 2, 2016 at 15:57
• @MauroALLEGRANZA Can you give me some sources to study up Urbain Le Verrier's discovery - specifically how it relates to falsifiability. –  user12196 Commented Jul 2, 2016 at 17:25

4 Answers 4

The best way to understand Popper is to read Popper. There are a few commentators who have correctly understood his ideas, but the vast bulk of commentary on Popper is not even able to state his ideas correctly. Lakatos, Feyerabend and Kuhn are especially bad and should be avoided.

To understand falsification properly, you need to understand Popper's theory of knowledge more broadly. Most philosophers of science who take science seriously and think it is good are inductivists: they believe in a process called induction. Induction supposedly involves (1) taking observations, (2) using them to make theories, and then (3) showing those theories are true or probably true by more observations. People have looked at many phenomena such as the night sky, biology, medicine and so on, without learning much for thousands of years. So just observing stuff doesn't do much good. If you don't know what to look for, just observing will not produce progress, so step (1) is impossible. In addition, explanations don't follow from observations. The theory of stars has implications for many events we will never observe, e.g. - supernovae that took place before there were human observers, and those events don't follow from observations without a theory of how stars change. So steps (2) and (3) are also impossible.

So if we don't get theories from observation how do we get them? We guess. You look for a problem: some issue that is not explained by current ideas. You guess solutions to that problem. You then criticise the proposed solutions. This criticism may involve experiments, but many theories can be eliminated without doing experiments, e.g. - inconsistent theories.

An experiment involves looking for a situation in which two or more different ideas about how the world works make different predictions. You then either set up that situation or look for an existing system that realises that situation. Newton's theory of gravity and Einstein's general theory of relativity made different predictions about Mercury, and Newton's theory was refuted.

Some philosophers make a lot of fuss about the possibility that you might do an experiment wrong or misinterpret the results. But as Popper pointed out in Logic of Scientific Discovery, Chapter V (especially Section 29), this problem is solved by his epistemology. If an experiment contradicts an existing theory, that's a problem. This problem can be solved by any guess that explains the difference and is not eliminated by some criticism. The discovery of Neptune was taken as an example above, so let's look at it. An unsolved problem was found in explaining the orbits of some planets. Urbain Le Verrier guessed that there might be another planet. He worked out some constraints on where the planet could be to produce such effects, Johann Gottfried Galle looked for it and found it. If Galle had not found the planet that problem would have remained unsolved. Perhaps some other explanation could have been found to reconcile Newtonian mechanics with observation, perhaps not. Popper recommended that a proposed solution to a scientific problem should be rejected if it was ad hoc: if it had no implications beyond the problem it was invented to solve.

I'm going to skip the heliocentric theory because it is fairly similar to Newtonian mechanics. If you want a long list of examples, see the introduction to "Realism and the Aim of Science" by Popper.

Mathematical theories are about abstractions. They can be critically discussed, but not experimentally tested. 1+1 = 2 even though it is possible to think of examples of putting two objects together and only getting one object as a result. If you move two piles of sand together, you may only get one pile. So you have to think carefully about what systems you take as models of mathematical operations such as addition. For a discussion see "Realism and the Aim of Science" by Popper Chapter III, Section 24.

As far as probability is concerned, the best existing explanations have been provided by David Deutsch, see

https://arxiv.org/abs/1508.02048 .

For explanations of Popper's positions, see "Objective Knowledge" by Popper, Chapter 1, "Realism and the Aim of Science" by Popper, "Logic of Scientific Discovery" by Popper, "The Fabric of Reality" by David Deutsch, Chapters 3 and 7, and "The Beginning of Infinity" by David Deutsch, Chapters 1,2,4 and 13.

In the comments to Cort Ammon's answer you say:

"So we can't falsify mathematical theories? I thought Popper's method was a way of distinguishing the scientific from the non scientific - does that imply mathematical constructs are not scientific or is there something wrong with Popper's method."

Exactly, mathematical theories are not scientific theories. Mathematics is about abstract mathematical objects, Science is about empirically observables phenomena. The truth of mathematical statements are prove using logic and reason alone, while the truth of statements in physics, chemistry, biology, etc...are proven by experiment and observation. This was best described by David Hume, with his distinction known as Hume's Fork :

"All the objects of human reason or enquiry may naturally be divided into two kinds, to wit, Relations of Ideas, and Matters of fact. Of the first kind are the sciences of Geometry, Algebra, and Arithmetic ... [which are] discoverable by the mere operation of thought ... Matters of fact, which are the second object of human reason, are not ascertained in the same manner; nor is our evidence of their truth, however great, of a like nature with the foregoing." - An Enquiry Concerning Human Understanding

So things like the fundamental theorem of calculus and probability theory can't be falsified because they don't correspond to anything observable. They, like all mathematical truths are proved solely using the rules and axioms of logic.

This is the whole point of falsification, one has to attempt to show that they empirically observe a phenomena that contradicts their theory. So the Newton's theory of gravity says that apples should fall every time we let go of them in midair. Pre Popper's falsificationism, Newton's theory is falsified if someone raises an apple lets go of it, and instead of it falling it hovers in the air or goes upwards.

Similarly per Popper, heliocentrism will be falsified the day that Venus or Mars, or one of the other planets is observed in a different orbit then the one predicted by the theory.

This points to an interesting problem with Popper's theory, that of auxiliary hypotheses (also called the Duhem-Quine thesis, or they idea that all observations are theory laden): Consider that at the beginning of the 19th century the orbit of Uranus was different than what was predicted by Newtoninan mechanics and heliocentrism. But astronomers, instead of abandoning the theory, concluded that there was an unknown planet modifying the orbit of Uranus, which they later confirmed and called Neptune. So the dilemma is: When observation contradicts theory, is the theory falsified? or is there missing data that can explain the mismatch between theory and predictions?

The issue of how to solve the problem of auxiallry hypotheses is still debated, and hasn't been solved yet. See the ideas of W.V.O Quine, Thomas Kuhn, Imre Lakatos and Paul Feyerabend, all in response to Popper's concept of falsification.

• The thing is that the wiki article also says arguments could be used to falsify-so I thought it might be applicable for mathematics constructs as well. –  user12196 Commented Jul 3, 2016 at 19:01

Falsification is an excellent and easy to understand system in principle, but much more nuanced in implementation. The easiest to falsify hypotheses are those famous ones such as "all swans are white," which can be falsified by observing a black swan. Of course, this assumes we all agree on what is a swan is. Hypotheses get murkier from there.

When it comes to real meaningful scientific hypotheses, falsification is typically more of an extended process rather than an instantaneous event. A scientific theory which is falsifiable is one where some results could cast substantial doubt on the hypothesis, and that doubt can be compounded by future tests.

For example, if one believed the hypothesis that light acts as a wave, one would be surprised to see particle like behavior. The photoelectric effect is one such effect that we now know exhibits particle like behavior. The first time one observes particle like behavior from an experiment, one might assume the results were a measurement error. Doing it a second time would begin casting doubt on the theory that light always behaves like a wave. Having dozens of scientists all run such experiments multiple times, and each discovering particle like effects would eventually "falsify" the hypothesis.

This process is even more complicated due to statistics. If I claim there is a gaussian error term on my results, you can never truly prove that my theory is wrong, because there is always a non-zero chance that you simply observed random luck. However, in practice, once the probability of such chance events is low enough, we declare a theory "falsified." How high one has to go is discipline dependent. In sociology, we regularly see error terms permitting 10% or even 20% due to unexplained factors. In particle physics, a hypothesis is not declared "confirmed" until those unexplained factors account for no more than 0.00001% of the total observed effects. This is because subatomic particles behave quite regularly, and we're able to generate as many results as needed to attain such high degrees of confidence. In sociology, it is much harder to repeat experiments and there is a great deal of variance between individuals, so the best we can do is lower degrees of confidence.

As for your list of examples:

It is generally accepted that the motion of planets is almost completely governed by gravitational interactions. If we were to observe the motion of the planets, and find substantial deviations not explained by his theory of gravity, this would either falsify his theory or show that there are other forces at work. I believe we actually do see results which would falsify his work: you have to account for relativity to explain some movements (particularly in cases near a black hole)

Heliocentralism actually cannot be proven nor disproven because it is merely a model. Its more akin to a coordinate system transform than a theory. However, if one assumes geocentralism, one is forced to admit many strange forces which account for all of the movement we see in the planets. If one assumes heliocentralism, the movement can be explained entirely with simple conservative gravity models like Newton's theory of gravity. It is the simplicity of the heliocentric model that made it so effective.

Consider if we couldn't go anywhere, because of Xeno's paradox. This would demonstrate that the assumptions we make regarding limits are false. That being said, calculus is a mathematical construct. All that we can really falsify is its usefulness in describing the world around us.

Once again this is a mathematical construct, making it difficult to falsify. However, one could argue that it is "falsified" by demonstrating that it does not effectively model reality. One major assumption in much of probability is IID: the idea that observations are (I)ndependent (I)denticaly (D)istributed. If there was a reason to argue that this assumption is invalid in the real world, then much of probability would not apply. This actually does occur when exploring the human mind. In many cases the assumption of IID is very poorly founded, so many simplifications that probability would permit are simply invalid when discussing the behavior of the mind.

• 1 So we can't falsify mathematical theories? I thought Popper's method was a way of distinguishing the scientific from the non scientific - does that imply mathematical constructs are not scientific or is there something wrong with Popper's method. Theories in physics use many mathematical constructs as well - so if they are not falsifiable how come the concepts on physics are? –  user12196 Commented Jul 3, 2016 at 8:33
• 1 @novice I cannot answer the question "does that imply mathematical constructs are not scientific" directly, because it would involve a detailed discussion of exactly what "not scientific" means to you . That would be better suited for a chat room, rather than comments. However, I will try my best to answer obliquely. The validity of a mathematical construct is based in the validity of its assumptions and the validity of the rules of inference associated with it, not physical reality. It is only as one seeks to apply said constructs to the real world that the concept of falsification... –  Cort Ammon Commented Jul 3, 2016 at 16:39
• 1 ... becomes meaningful. Without that application to physical reality, mathematical theories are subject to far more stringent validity requirements than falsification would ever ask from them. However, when applying such constructs to science, one does make the assumption that those constructs are indeed valid. If the real numbers do not, indeed, form a field, calculus falls apart. On the other hand, it is totally valid to have mathematical constructs which do not have an immediately obvious connection to reality. The concept of complex numbers, for instance, was a mathematical... –  Cort Ammon Commented Jul 3, 2016 at 16:41
• 1 ... curiosity until Euler's function connected them to cyclic motion. Now they are used constantly in science. If you are interested in this question of the validity of mathematical construct, I recommend a beautiful video by Vsauce, How to Count past Infinity . He does an excellent job of explaining the subtle distinction between scientific theories and mathematical ones. –  Cort Ammon Commented Jul 3, 2016 at 16:44
• Thanks for your comments as well as the resource you have provided. I confess I am not sure how Popper gives arguments/justifications in favor of the notion of falsifiability to distinguish science from pseudo-science. I read the part about observation and argument - and thought that an argument for the falsifiability of mathematical constructs must be possible - instead of an observation. Let me go into this more deeply and come back with more questions. –  user12196 Commented Jul 3, 2016 at 17:20

I'm not sure just how useful falsification is in explaining the actual progress of science, in the sense of the revision of its basic concepts; its not for example a source of new ideas, but of pruning out what is given on the basis of what is known. Its a minor mode of progress, and not its major mode. A major mode would tell us how to find new ideas, unfortunately such a philosphical stone is illusionary.

For example, calculus can be explained by attempting to give meaning to 0/0; this, in terms of the usual arithmetic operations, is nonsensical; however mathematicians like to 'close up' operations; 0/0 can in fact be given meaning by thinking of it as dx/dy; of course this opens up the whole new world of calculus.

Similarly, no meaning could be given to the square root of -1; eventually one was found that was useful: i -the imaginary; and it again opened up a whole new world of complex geometry.

Probability is a concept with intuitive appeal; yet Quantum Mechanics relies on the notion of the square root of probability, and in fact a great deal of the bizarre beahviour can be explained on the basis of this new concept which still hasn't found a properly ontological basis in the same way that the infinitesimal or the imaginary has.

• -1 This answer does not even attempt to address the question. –  MmmHmm Commented Jun 23, 2018 at 15:15

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scientific hypothesis

Our editors will review what you’ve submitted and determine whether to revise the article.

• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - Open Science - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

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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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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. 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 provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

5 Falsifiability

Textbook chapters (or similar texts).

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

Journal articles

• 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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The Discovery of the Falsifiability Principle

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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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Being Scientific: Falsifiability, Verifiability, Empirical Tests, and Reproducibility

If you ask a scientist what makes a good experiment, you’ll get very specific answers about reproducibility and controls and methods of teasing out causal relationships between variables and observables. If human observations are involved, you may get detailed descriptions of blind and double-blind experimental designs. In contrast, if you ask the very same scientists what makes a theory or explanation scientific, you’ll often get a vague statement about falsifiability . Scientists are usually very good at designing experiments to test theories. We invent theoretical entities and explanations all the time, but very rarely are they stated in ways that are falsifiable. It is also quite rare for anything in science to be stated in the form of a deductive argument. Experiments often aren’t done to falsify theories, but to provide the weight of repeated and varied observations in support of those same theories. Sometimes we’ll even use the words verify or confirm when talking about the results of an experiment. What’s going on? Is falsifiability the standard? Or something else?

The difference between falsifiability and verifiability in science deserves a bit of elaboration. It is not always obvious (even to scientists) what principles they are using to evaluate scientific theories, 1 so we’ll start a discussion of this difference by thinking about Popper’s asymmetry. 2 Consider a scientific theory ( T ) that predicts an observation ( O ). There are two ways we could approach adding the weight of experiment to a particular theory. We could attempt to falsify or verify the observation. Only one of these approaches (falsification) is deductively valid:

Popper concluded that it is impossible to know that a theory is true based on observations ( O ); science can tell us only that the theory is false (or that it has yet to be refuted). He concluded that meaningful scientific statements are falsifiable.

Scientific theories may not be this simple. We often base our theories on a set of auxiliary assumptions which we take as postulates for our theories. For example, a theory for liquid dynamics might depend on the whole of classical mechanics being taken as a postulate, or a theory of viral genetics might depend on the Hardy-Weinberg equilibrium. In these cases, classical mechanics (or the Hardy-Wienberg equilibrium) are the auxiliary assumptions for our specific theories.

These auxiliary assumptions can help show that science is often not a deductively valid exercise. The Quine-Duhem thesis 3 recovers the symmetry between falsification and verification when we take into account the role of the auxiliary assumptions ( AA ) of the theory ( T ):

That is, if the predicted observation ( O ) turns out to be false, we can deduce only that something is wrong with the conjunction, ( T and AA ); we cannot determine from the premises that it is T rather than AA that is false. In order to recover the asymmetry, we would need our assumptions ( AA ) to be independently verifiable:

Falsifying a theory requires that auxiliary assumption ( AA ) be demonstrably true. Auxiliary assumptions are often highly theoretical — remember, auxiliary assumptions might be statements like the entirety of classical mechanics is correct or the Hardy-Weinberg equilibrium is valid ! It is important to note, that if we can’t verify AA , we will not be able to falsify T by using the valid argument above. Contrary to Popper, there really is no asymmetry between falsification and verification. If we cannot verify theoretical statements, then we cannot falsify them either.

Since verifying a theoretical statement is nearly impossible, and falsification often requires verification of assumptions, where does that leave scientific theories? What is required of a statement to make it scientific?

Carl Hempel came up with one of the more useful statements about the properties of scientific theories: 4 “The statements constituting a scientific explanation must be capable of empirical test.” And this statement about what exactly it means to be scientific brings us right back to things that scientists are very good at: experimentation and experimental design. If I propose a scientific explanation for a phenomenon, it should be possible to subject that theory to an empirical test or experiment. We should also have a reasonable expectation of universality of empirical tests. That is multiple independent (skeptical) scientists should be able to subject these theories to similar tests in different locations, on different equipment, and at different times and get similar answers. Reproducibility of scientific experiments is therefore going to be required for universality.

So to answer some of the questions we might have about reproducibility:

• Reproducible by whom ? By independent (skeptical) scientists, working elsewhere, and on different equipment, not just by the original researcher.
• Reproducible to what degree ? This would depend on how closely that independent scientist can reproduce the controllable variables, but we should have a reasonable expectation of similar results under similar conditions.
• Wouldn’t the expense of a particular apparatus make reproducibility very difficult? Good scientific experiments must be reproducible in both a conceptual and an operational sense. 5 If a scientist publishes the results of an experiment, there should be enough of the methodology published with the results that a similarly-equipped, independent, and skeptical scientist could reproduce the results of the experiment in their own lab.

Computational science and reproducibility

If theory and experiment are the two traditional legs of science, simulation is fast becoming the “third leg”. Modern science has come to rely on computer simulations, computational models, and computational analysis of very large data sets. These methods for doing science are all reproducible in principle . For very simple systems, and small data sets this is nearly the same as reproducible in practice . As systems become more complex and the data sets become large, calculations that are reproducible in principle are no longer reproducible in practice without public access to the code (or data). If a scientist makes a claim that a skeptic can only reproduce by spending three decades writing and debugging a complex computer program that exactly replicates the workings of a commercial code, the original claim is really only reproducible in principle. If we really want to allow skeptics to test our claims, we must allow them to see the workings of the computer code that was used. It is therefore imperative for skeptical scientific inquiry that software for simulating complex systems be available in source-code form and that real access to raw data be made available to skeptics.

Our position on open source and open data in science was arrived at when an increasing number of papers began crossing our desks for review that could not be subjected to reproducibility tests in any meaningful way. Paper A might have used a commercial package that comes with a license that forbids people at university X from viewing the code ! 6

Paper 2 might use a code which requires parameter sets that are “trade secrets” and have never been published in the scientific literature . Our view is that it is not healthy for scientific papers to be supported by computations that cannot be reproduced except by a few employees at a commercial software developer. Should this kind of work even be considered Science? It may be research , and it may be important , but unless enough details of the experimental methodology are made available so that it can be subjected to true reproducibility tests by skeptics, it isn’t Science.

• This discussion closely follows a treatment of Popper’s asymmetry in: Sober, Elliot Philosophy of Biology (Boulder: Westview Press, 2000), pp. 50-51.
• Popper, Karl R. “The Logic of Scientific Discovery” 5th ed. (London: Hutchinson, 1959), pp. 40-41, 46.
• Gillies, Donald. “The Duhem Thesis and the Quine Thesis”, in Martin Curd and J.A. Cover ed. Philosophy of Science: The Central Issues, (New York: Norton, 1998), pp. 302-319.
• C. Hempel. Philosophy of Natural Science 49 (1966).
• Lett, James, Science, Reason and Anthropology, The Principles of Rational Inquiry (Oxford: Rowman & Littlefield, 1997), p. 47
• See, for example www.bannedbygaussian.org

5 Responses to Being Scientific: Falsifiability, Verifiability, Empirical Tests, and Reproducibility

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“If we cannot verify theoretical statements, then we cannot falsify them either.

Since verifying a theoretical statement is nearly impossible, and falsification often requires verification of assumptions…”

An invalid argument is invalid regardless of the truth of the premises. I would suggest that an hypothesis based on unverifiable assumptions could be ‘falsified’ the same way an argument with unverifiable premises could be shown to be invalid. Would you not agree?

“Falsifying a theory requires that auxiliary assumption (AA) be demonstrably true.”

No, it only requires them to be true.

In the falisificationist method, you can change the AA so long as that increases the theories testability. (the theory includes AA and the universal statement, btw) . In your second box you misrepresent the first derivation. in the conclusion it would be ¬(t and AA). after that you can either modify the AA (as long as it increase the theories falsifiability) or abandon the theory. Therefore you do not need the third box, it explains something that does not need explaining, or that could be explained more concisely and without error by reconstructing the process better. This process is always tentative and open to re-evaluation (that is the risky and critical nature of conjectures and refutations). Falsificationism does not pretend conclusiveness, it abandoned that to the scrap heap along with the hopelessly defective interpretation of science called inductivism.

“Contrary to Popper, there really is no asymmetry between falsification and verification. If we cannot verify theoretical statements, then we cannot falsify them either.” There is an asymmetry. You cannot refute the asymmetry by showing that falsification is not conclusive. Because the asymmetry is a logical relationship between statements. What you would have shown, if your argument was valid or accurate, would be that falsification is not possible in practice. Not that the asymmetry is false.

Popper wanted to replace induction and verification with deduction and falsification.

He held that a theory that was once accepted but which, thanks to a novel experiment or observation, turns out to be false, confronts us with a new problem, to which new solutions are needed. In his view, this process is the hallmark of scientific progress.

Surprisingly, Popper failed to note that, despite his efforts to present it as deductive, this process is at bottom inductive, since it assumes that a theory falsified today will remain falsified tomorrow.

Accepting that swans are either white or black because a black one has been spotted rests on the assumption that there are other black swans around and that the newly discovered black one will not become white at a later stage. It is obvious but also inductive thinking in the sense that they project the past into the future, that is, extrapolate particulars into a universal.

In other words, induction, the process that Popper was determined to avoid, lies at the heart of his philosophy of science as he defined it.

Despite positivism’s limitations, science is positive or it is not science : positive science’s theories are maybe incapable of demonstration (as Hume wrote of causation), but there are not others available.

If it is impossible to demonstrate that fire burns, putting one’s hand in it is just too painful.

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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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What is a scientific hypothesis?

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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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The Unfalsifiable Hypothesis Paradox

What is the unfalsifiable hypothesis paradox.

Imagine someone tells you a story about a dragon that breathes not fire, but invisible, heatless fire. You grab a thermometer to test the claim but no matter what, you can’t prove it’s not true because you can’t measure something that’s invisible and has no heat. This is what we call an ‘unfalsifiable hypothesis’—it’s a claim that’s made in such a way that it can’t be proven wrong, no matter what.

Now, the paradox is this: in science, being able to prove or disprove a claim makes it strong and believable. If nobody could ever prove a hypothesis wrong, you’d think it’s completely reliable, right? But actually, in science, that makes it weak! If we can’t test a claim, then it’s not really playing by the rules of science. So, the paradox is that not being able to prove something wrong can make a claim scientifically useless—even though it seems like it would be the ultimate truth.

Key Arguments

• An unfalsifiable hypothesis is a claim that can’t be proven wrong, but just because we can’t disprove it, that doesn’t make it automatically true.
• Science grows and improves through testing ideas; if we can’t test a claim, we can’t know if it’s really valid.
• Being able to show that an idea could be wrong is a fundamental part of scientific thinking. Without this testability, a claim is more like a personal belief or a philosophical idea than a scientific one.
• An unfalsifiable hypothesis might look like it’s scientific, but it’s misleading since it doesn’t stick to the strict rules of testing and evidence that science needs.
• Using unfalsifiable claims can block our paths to understanding since they stop us from asking questions and looking for verifiable answers.
• The dragon with invisible, heatless fire: This is an example of an unfalsifiable hypothesis because no test or observation could ever show that the dragon’s fire isn’t real, since it can’t be detected in any way.
• Saying a celestial teapot orbits the Sun between Earth and Mars: This teapot is said to be small and far enough away that no telescope could spot it. Because it’s undetectable, we can’t disprove its existence.
• A theory that angels are responsible for keeping us gravitationally bound to Earth: Since we can’t test for the presence or actions of angels, we can’t refute the claim, making it unfalsifiable.
• The statement that the world’s sorrow is caused by invisible spirits: It sounds serious, but if we can’t measure or observe these spirits, we can’t possibly prove this idea right or wrong.

Answer or Resolution

Dealing with the Unfalsifiable Hypothesis Paradox means finding a balance. We can’t just ignore all ideas that can’t be tested because some might lead to real scientific breakthroughs one day. On the other side, we can’t treat untestable claims as true science. It’s about being open to possibilities but also clear about what counts as scientific evidence.

Some people might say we should only focus on what can be proven wrong. Others think even wild ideas have their place at the starting line of science—they inspire us and can evolve into something testable later on.

Major Criticism

Some people criticize the idea of rejecting all unfalsifiable ideas because that could block new ways of thinking. Sometimes a wild guess can turn into a real scientific discovery. Plus, falsifiability is just one part of what makes a theory scientific. We shouldn’t throw away potentially good ideas just because they don’t fit one rule, especially when they’re still in the early stages and shouldn’t be held too tightly to any rules at all.

Another point is that some important ideas have been unfalsifiable at first but later became testable. So, we have to recognize that science itself can change and grow.

Practical Applications

You might wonder, “Why does this matter to me?” Well, knowing about the Unfalsifiable Hypothesis Paradox actually affects a lot of real-world situations, like how we learn things in school, the kinds of products we buy, and even the rules and laws that are made.

• Education: By learning what makes science solid, students can tell the difference between real science and just a bunch of fancy words that sound scientific but aren’t based on testable ideas.
• Consumer Protection: Sometimes companies try to sell things by using science-sounding claims that can’t be proven wrong—and that’s where knowing about unfalsifiable hypotheses helps protect us from buying into false promises.
• Legal and Policy Making: For people who make laws or guide big decisions, understanding this concept helps them judge if a study or report is really based on solid science.

Related Topics

The Unfalsifiable Hypothesis Paradox is linked with a couple of other important ideas you might hear about:

• Scientific Method: This is the set of steps scientists use to learn about the world. Part of the process is making sure ideas can be tested.
• Pseudoscience: These are beliefs or practices that try to appear scientific but don’t follow the scientific method properly, often using unfalsifiable claims.
• Empiricism : This big word just means learning by observation and experiment—the backbone of science and everything opposite of unfalsifiable concepts.

Wrapping up, the Unfalsifiable Hypothesis Paradox shows us that science isn’t just about coming up with ideas—it’s about being able to test them, too. Untestable claims may be interesting, but they can’t help us understand the world in a scientific way. But remember, just because an idea is unfalsifiable now doesn’t mean it will be forever. The best approach is using that creative spark but always grounding it in what we can observe and prove. This balance keeps our imaginations soaring but our facts checked, forming a bridge between our wildest ideas and the world we can measure and know.

From the Editors

Notes from The Conversation newsroom

How we edit science part 1: the scientific method

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We take science seriously at The Conversation and we work hard to report it accurately. This series of five posts is adapted from an internal presentation on how to understand and edit science by our Australian Science & Technology Editor, Tim Dean. We thought you might also find it useful.

Introduction

If I told you that science was a truth-seeking endeavour that uses a single robust method to prove scientific facts about the world, steadily and inexorably driving towards objective truth, would you believe me?

Many would. But you shouldn’t.

The public perception of science is often at odds with how science actually works. Science is often seen to be a separate domain of knowledge, framed to be superior to other forms of knowledge by virtue of its objectivity, which is sometimes referred to as it having a “ view from nowhere ”.

But science is actually far messier than this - and far more interesting. It is not without its limitations and flaws, but it’s still the most effective tool we have to understand the workings of the natural world around us.

In order to report or edit science effectively - or to consume it as a reader - it’s important to understand what science is, how the scientific method (or methods) work, and also some of the common pitfalls in practising science and interpreting its results.

This guide will give a short overview of what science is and how it works, with a more detailed treatment of both these topics in the final post in the series.

What is science?

Science is special, not because it claims to provide us with access to the truth, but because it admits it can’t provide truth .

Other means of producing knowledge, such as pure reason, intuition or revelation, might be appealing because they give the impression of certainty , but when this knowledge is applied to make predictions about the world around us, reality often finds them wanting.

Rather, science consists of a bunch of methods that enable us to accumulate evidence to test our ideas about how the world is, and why it works the way it does. Science works precisely because it enables us to make predictions that are borne out by experience.

Science is not a body of knowledge. Facts are facts, it’s just that some are known with a higher degree of certainty than others. What we often call “scientific facts” are just facts that are backed by the rigours of the scientific method, but they are not intrinsically different from other facts about the world.

What makes science so powerful is that it’s intensely self-critical. In order for a hypothesis to pass muster and enter a textbook, it must survive a battery of tests designed specifically to show that it could be wrong. If it passes, it has cleared a high bar.

The scientific method(s)

Despite what some philosophers have stated , there is a method for conducting science. In fact, there are many. And not all revolve around performing experiments.

One method involves simple observation, description and classification, such as in taxonomy. (Some physicists look down on this – and every other – kind of science, but they’re only greasing a slippery slope .)

However, when most of us think of The Scientific Method, we’re thinking of a particular kind of experimental method for testing hypotheses.

This begins with observing phenomena in the world around us, and then moves on to positing hypotheses for why those phenomena happen the way they do. A hypothesis is just an explanation, usually in the form of a causal mechanism: X causes Y. An example would be: gravitation causes the ball to fall back to the ground.

A scientific theory is just a collection of well-tested hypotheses that hang together to explain a great deal of stuff.

Crucially, a scientific hypothesis needs to be testable and falsifiable .

An untestable hypothesis would be something like “the ball falls to the ground because mischievous invisible unicorns want it to”. If these unicorns are not detectable by any scientific instrument, then the hypothesis that they’re responsible for gravity is not scientific.

An unfalsifiable hypothesis is one where no amount of testing can prove it wrong. An example might be the psychic who claims the experiment to test their powers of ESP failed because the scientific instruments were interfering with their abilities.

(Caveat: there are some hypotheses that are untestable because we choose not to test them. That doesn’t make them unscientific in principle, it’s just that they’ve been denied by an ethics committee or other regulation.)

Experimentation

There are often many hypotheses that could explain any particular phenomenon. Does the rock fall to the ground because an invisible force pulls on the rock? Or is it because the mass of the Earth warps spacetime , and the rock follows the lowest-energy path, thus colliding with the ground? Or is it that all substances have a natural tendency to fall towards the centre of the Universe , which happens to be at the centre of the Earth?

The trick is figuring out which hypothesis is the right one. That’s where experimentation comes in.

A scientist will take their hypothesis and use that to make a prediction, and they will construct an experiment to see if that prediction holds. But any observation that confirms one hypothesis will likely confirm several others as well. If I lift and drop a rock, it supports all three of the hypotheses on gravity above.

Furthermore, you can keep accumulating evidence to confirm a hypothesis, and it will never prove it to be absolutely true. This is because you can’t rule out the possibility of another similar hypothesis being correct, or of making some new observation that shows your hypothesis to be false. But if one day you drop a rock and it shoots off into space, that ought to cast doubt on all of the above hypotheses.

So while you can never prove a hypothesis true simply by making more confirmatory observations, you only one need one solid contrary observation to prove a hypothesis false. This notion is at the core of the hypothetico-deductive model of science.

This is why a great deal of science is focused on testing hypotheses, pushing them to their limits and attempting to break them through experimentation. If the hypothesis survives repeated testing, our confidence in it grows.

So even crazy-sounding theories like general relativity and quantum mechanics can become well accepted, because both enable very precise predictions, and these have been exhaustively tested and come through unscathed.

The next post will cover hypothesis testing in greater detail.

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What is falsifiability?

Falsifiability is the capacity for some proposition, statement, theory or hypothesis to be proven wrong. The concept of falsifiability was introduced in 1935 by Austrian philosopher and scientist Karl Popper (1902-1994). Since then, the scientific community has come to consider falsifiability to be one of the fundamental tenets of the scientific method , along with attributes such as replicability and testability.

A scientific hypothesis, according to the doctrine of falsifiability, is credible only if it is inherently falsifiable. This means that the hypothesis must be capable of being tested and proven wrong. It does not automatically mean that the hypothesis is invalid or incorrect, only that the potential exists for the hypothesis to be refuted at some possible time or place.

For example, one could hypothesize that a divine being with green scales, mauve hair, ochre-colored teeth and a propensity for humming show tunes rules over the physical universe from a different dimension. Even if millions of people were to swear their allegiance to such a being, there is no practical way to disprove this hypothesis, which means that it is not falsifiable. As a result, it cannot be considered a scientific assertion, according to the rules of falsifiability.

On the other hand, Einstein's theory of relativity is considered credible science according to these rules because it could be proven incorrect at some point in time through scientific experimentation and advanced testing techniques, especially as the methods continue to expand our body of knowledge. In fact, it's already widely accepted that Einstein's theory is at odds with the fundamentals of quantum mechanics, not unlike the way Newton's theory of gravity could not fully account for Mercury's orbit.

Another implication of falsifiability is that conclusions should not be drawn from simple observations of a particular phenomenon . The white swan hypothesis illustrates this problem. For many centuries, Europeans saw only white swans in their surroundings, so they assumed that all swans were white. However, this theory is clearly falsifiable because it takes the discovery of only one non-white swan to disprove its hypothesis, which is exactly what occurred when Dutch explorers found black swans in Australia in the late 17th century.

Falsifiability is often closely linked with the idea of the null hypothesis in hypothesis testing. The null hypothesis states the contrary of an alternative hypothesis. It provides the basis of falsifiability, describing what the outcome would demonstrate if the prediction of the alternative hypothesis is not supported. The alternative hypothesis might predict, for example, that fewer work hours correlates to lower employee productivity. A null hypothesis might propose that fewer work hours correlates with higher productivity or that there is no change in productivity when employees spend less time at work.

Popper makes the case for falsifiability

Karl Popper introduced the concept of falsifiability in his book The Logic of Scientific Discovery (first published in German in 1935 under the title Logik der Forschung ). The book centered on the demarcation problem, which explored the difficulty of separating science from pseudoscience . Popper claimed that only if a theory is falsifiable can it be considered scientific. In contrast, areas of study such as astrology, Marxism or even psychoanalysis were merely pseudosciences.

Popper's theories on falsifiability and pseudoscience have had a significant impact on what is now considered to be true science. Even so, there is no universal agreement about the role of falsifiability in science because of the limitations inherent in testing any hypothesis. Part of this comes from the fact that testing a hypothesis often brings its own set of assumptions, as well as an inability to account for all the factors that could potentially impact the outcome of a test, putting the test in question as much as the original hypothesis.

In addition, the tests we have at hand might be approaching their practical limitations when up against hypotheses such as string theory or multiple universes. It might not be possible to ever fully test such hypotheses to the degree envisioned by Popper. The question also arises whether falsifiability has anything to do with actual scientific discovery or whether the theory of falsification is itself falsifiable.

No doubt many researchers would argue that their brand of social or psychological science meets a set of criteria that is equally viable as those laid out by Popper. Even so, the important role that falsifiability has played in the scientific model cannot be denied, but Popper's black-and-white demarcation between science and pseudoscience might need to give way to a more comprehensive perspective of what we understand as being scientific.

See also:  empirical analysis ,  validated learning ,  OODA loop , black swan event,  deep learning .

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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.
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A BLOG ABOUT SCIENCE IN A WORLD OF UNTRUE FACTS

When you can never be wrong: the unfalsifiable hypothesis

• February 9, 2021

By Kristen Panthagani, PhD

If there was one single scientific concept I could teach everyone in the country right now it would be this: what is an  unfalsifiable hypothesis , and why do they confuse everyone.

This concept alone explains a lot of the confusion and conspiracy theories around the COVID pandemic… why many still insist that Bill Gates was involved in planning the pandemic or that there are microchips in vaccines. 

What is a hypothesis?

Before we get to unfalsifiable hypotheses, let’s start with what a hypothesis is. In very simple terms, a hypothesis is a tentative explanation that needs to be tested . It’s an idea formed on the available evidence that is maybe true, but still needs to be explored and verified. For example, at the beginning of the pandemic, many had the hypothesis that hydroxychloroquine is an effective treatment for COVID.  

Hypotheses are the jumping off points of scientific experiments. They define what question we want to test. And that brings us to one of the most important qualities of a valid scientific hypothesis: they must actually be testable. Or said another way,  they must be falsifiable.

What is a falsifiable hypothesis?

What does it mean for a hypothesis to be falsifiable? It means that we can actually design an experiment to test if it’s wrong (false).  For a hypothesis to be falsifiable, we must be able to design a test that provides us with one of three possible outcomes:

1. the results support the hypothesis,* or

2. the results are inconclusive, or 

3. the results reject the hypothesis. 

When the results reject our hypothesis, it tells us our hypothesis is wrong, and we move on.

*If we want to be nitpicky, instead of saying the results ‘support’ our hypothesis we should really say ‘the results fail to disprove our hypothesis.’ But, that’s beyond the scope of what you need to know for this post.

When the results reject our hypothesis, it tells us our hypothesis is wrong, and we move on. Tweet

That is the hallmark of a falsifiable hypothesis: you can find out when you’re wrong. So then, what is an unfalsifiable hypothesis? It is a hypothesis that is impossible to disprove . And it is not impossible to disprove because it’s correct, it’s impossible to disprove because there is no way to conclusively test it. For unfalsifiable hypotheses, every test you run will come up with not three, but two possible outcomes: 

1. the results support the hypothesis or

2. the results are inconclusive. 

‘ Results reject the hypothesis ‘  is missing. No amount of testing will ever lead to data that conclusively rejects the hypothesis, even if the hypothesis is completely wrong.

For unfalsifiable hypotheses that happen to be true (i.e. love exists), this is not a huge issue, because it’s usually pretty obvious that they’re right, despite their unfalsifiability. The problem arises for unfalsifiable hypotheses that are more tenuous claims.

In these cases, people may deeply believe they’re right, in part, because it is impossible to find conclusive evidence that they’re wrong.   Every time they try to test if their claim is true, they only find inconclusive evidence. And again, this is not because the hypothesis is correct, it’s because the hypothesis is set up in a way where a definitive “no that’s wrong” is impossible to find. A great example is the hypothesis that there are microchips in the vaccines. You could say ‘well just look in one and see if it’s there!’ And somebody checks and finds no microchip. End of story? Well no.. someone could argue ‘well the microchips are just too small to detect!’ or ‘They will know to take it out of the vials before they are scanned!’ Excuses are made so that the negative results are no longer negative results, but instead are inconclusive. Thus every possible result from any test we do can be deemed inconclusive by those who believe the hypothesis is correct. This makes the hypothesis, for the sake of the people who believe in it, unfalsifiable. This is why conspiracy theories are so hard to debunk… many of them are unfalsifiable hypotheses.

Why do these trap people so effectively? Two reasons. First, for a believer of the hypothesis, all they see is inconclusive data (which they can usually make fit their narrative). They never see any data disproving it, so it makes it easy for them to believe they’re right. And second, because it’s impossible to conclusively disprove it, we can’t go and… conclusively disprove it. This makes it easy for people to stay trapped in an unfalsifiable hypothesis they want to believe in, even when it’s 100% wrong.

So how do you know if you’ve been trapped into believing an unfalsifiable hypothesis? Ask yourself… how would I know if this was false? What evidence would come forward that would convince me? If the answer is ‘ well, I’m waiting for the results of this study to decide ‘ or ‘ I’m waiting for the outcome of this particular event to know ,’ then that suggests you’re not trapped in an unfalsifiable hypothesis, as you are open to actual evidence showing you that you’re wrong. (But, only if you do actually change your mind if that evidence fails to support your hypothesis, rather than finding an excuse why that event or evidence doesn’t actually disprove it.)

But, if the answer relies not on specific events or outcomes but primarily on the opinion of other believers, then you may be trapped in an unfalsifiable hypothesis, because that isn’t evidence… it’s just group think.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Understanding Science

How science REALLY works...

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Correcting misconceptions.

Many students have misconceptions about what science is and how it works. This section explains and corrects some of the most common misconceptions that students are likely have trouble with. If you are interested in common misconceptions about  teaching  the nature and process of science, visit our page on that topic .

Jump to: Misinterpretations of the scientific process | Misunderstandings of the limits of science | Misleading stereotypes of scientists | Vocabulary mix-ups | Roadblocks to learning science

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Misinterpretations of the scientific process

Misconception: science is a collection of facts..

CORRECTION:

Because science classes sometimes revolve around dense textbooks, it’s easy to think that’s all there is to science: facts in a textbook. But that’s only part of the picture. Science  is  a body of knowledge that one can learn about in textbooks, but it is also a process. Science is an exciting and dynamic process for discovering how the world works and building that knowledge into powerful and coherent frameworks. To learn more about the process of science, visit our section on  How science works .

MISCONCEPTION: Science is complete.

Since much of what is taught in introductory science courses is knowledge that was constructed in the 19th and 20th centuries, it’s easy to think that science is finished — that we’ve already discovered most of what there is to know about the natural world . This is far from accurate. Science is an ongoing process, and there is much more yet to learn about the world. In fact, in science, making a key discovery often leads to many new questions ripe for investigation. Furthermore, scientists are constantly elaborating, refining, and revising established scientific ideas based on new evidence and perspectives. To learn more about this, visit our page describing how scientific ideas lead to ongoing research .

MISCONCEPTION: There is a single Scientific Method that all scientists follow.

“The Scientific Method” is often taught in science courses as a simple way to understand the basics of scientific testing. In fact, the Scientific Method represents how scientists usually write up the results of their studies (and how a few investigations are actually done), but it is a grossly oversimplified representation of how scientists generally build knowledge. The process of science is exciting, complex, and unpredictable. It involves many different people, engaged in many different activities, in many different orders. To review a more accurate representation of the process of science, explore our  flowchart .

MISCONCEPTION: The process of science is purely analytic and does not involve creativity.

Perhaps because the Scientific Method presents a linear and rigid representation of the process of science, many people think that doing science involves closely following a series of steps, with no room for creativity and inspiration. In fact, many scientists recognize that creative thinking is one of the most important skills they have — whether that creativity is used to come up with an alternative hypothesis, to devise a new way of testing an idea, or to look at old data in a new light. Creativity is critical to science!

MISCONCEPTION: When scientists analyze a problem, they must use either inductive or deductive reasoning.

Scientists use all sorts of different reasoning modes at different times — and sometimes at the same time — when analyzing a problem. They also use their creativity to come up with new ideas, explanations, and tests. This isn’t an either/or choice between induction and deduction. Scientific analysis often involves jumping back and forth among different modes of reasoning and creative brainstorming! What’s important about scientific reasoning is not what all the different modes of reasoning are called, but the fact that the process relies on careful, logical consideration of how evidence supports or does not support an idea, of how different scientific ideas are related to one another, and of what sorts of things we can expect to observe if a particular idea is true. If you are interested in learning about the difference between induction and deduction, visit our  FAQ on the topic .

MISCONCEPTION: Experiments are a necessary part of the scientific process. Without an experiment, a study is not rigorous or scientific.

Perhaps because the Scientific Method and popular portrayals of science emphasize  experiments , many people think that science can’t be done  without  an experiment. In fact, there are  many  ways to test almost any scientific idea; experimentation is only one approach. Some ideas are best tested by setting up a  controlled experiment  in a lab, some by making detailed observations of the natural world, and some with a combination of strategies. To study detailed examples of how scientific ideas can be tested fairly, with and without experiments, check out our side trip  Fair tests: A do-it-yourself guide .

MISCONCEPTION: "Hard" sciences are more rigorous and scientific than "soft" sciences.

Some scientists and philosophers have tried to draw a line between “hard” sciences (e.g., chemistry and physics) and “soft” ones (e.g., psychology and sociology). The thinking was that hard science used more rigorous, quantitative methods than soft science did and so were more trustworthy. In fact, the rigor of a scientific study has much more to do with the investigator’s approach than with the discipline. Many psychology studies, for example, are carefully controlled, rely on large sample sizes, and are highly quantitative. To learn more about how rigorous and fair tests are designed, regardless of discipline, check out our side trip  Fair tests: A do-it-yourself guide .

MISCONCEPTION: Scientific ideas are absolute and unchanging.

Because science textbooks change very little from year to year, it’s easy to imagine that scientific ideas don’t change at all. It’s true that some scientific ideas are so well established and supported by so many lines of evidence, they are unlikely to be completely overturned. However, even these established ideas are subject to modification based on new evidence and perspectives. Furthermore, at the cutting edge of scientific research — areas of knowledge that are difficult to represent in introductory textbooks — scientific ideas may change rapidly as scientists test out many different possible explanations trying to figure out which are the most accurate. To learn more about this, visit our page describing  how science aims to build knowledge .

MISCONCEPTION: Because scientific ideas are tentative and subject to change, they can't be trusted.

Especially when it comes to scientific findings about health and medicine, it can sometimes seem as though scientists are always changing their minds. One month the newspaper warns you away from chocolate’s saturated fat and sugar; the next month, chocolate companies are bragging about chocolate’s antioxidants and lack of trans-fats. There are several reasons for such apparent reversals. First, press coverage tends to draw particular attention to disagreements or ideas that conflict with past views. Second, ideas at the cutting edge of research (e.g., regarding new medical studies) may change rapidly as scientists test out many different possible explanations trying to figure out which are the most accurate. This is a normal and healthy part of the process of science. While it’s true that all scientific ideas are subject to change if warranted by the evidence, many scientific ideas (e.g., evolutionary theory, foundational ideas in chemistry) are supported by many lines of evidence, are extremely reliable, and are unlikely to change. To learn more about provisionality in science and its portrayal by the media, visit a section from our  Science Toolkit .

MISCONCEPTION: Scientists' observations directly tell them how things work (i.e., knowledge is "read off" nature, not built).

Because science relies on observation and because the process of science is unfamiliar to many, it may seem as though scientists build knowledge directly through observation. Observation  is  critical in science, but scientists often make  inferences  about what those observations mean. Observations are part of a complex process that involves coming up with ideas about how the natural world works and seeing if observations back those explanations up. Learning about the inner workings of the natural world is less like reading a book and more like writing a non-fiction book — trying out different ideas, rephrasing, running drafts by other people, and modifying text in order to present the clearest and most accurate explanations for what we observe in the natural world. To learn more about how scientific knowledge is built, visit our section  How science works .

MISCONCEPTION: Science proves ideas.

Journalists often write about “scientific proof” and some scientists talk about it, but in fact, the concept of proof — real, absolute proof — is not particularly scientific. Science is based on the principle that  any  idea, no matter how widely accepted today, could be overturned tomorrow if the evidence warranted it. Science accepts or rejects ideas based on the evidence; it does not prove or disprove them. To learn more about this, visit our page describing  how science aims to build knowledge .

MISCONCEPTION: Science can only disprove ideas.

This misconception is based on the idea of falsification, philosopher Karl Popper’s influential account of scientific justification, which suggests that all science can do is reject, or falsify, hypotheses — that science cannot find evidence that  supports  one idea over others. Falsification was a popular philosophical doctrine — especially with scientists — but it was soon recognized that falsification wasn’t a very complete or accurate picture of how scientific knowledge is built. In science, ideas can never be completely proved or completely disproved. Instead, science accepts or rejects ideas based on supporting and refuting evidence, and may revise those conclusions if warranted by new evidence or perspectives.

MISCONCEPTION: If evidence supports a hypothesis, it is upgraded to a theory. If the theory then garners even more support, it may be upgraded to a law.

This misconception may be reinforced by introductory science courses that treat hypotheses as “things we’re not sure about yet” and that only explore established and accepted theories. In fact, hypotheses, theories, and laws are rather like apples, oranges, and kumquats: one cannot grow into another, no matter how much fertilizer and water are offered. Hypotheses, theories, and laws are all scientific explanations that differ in breadth — not in level of support. Hypotheses are explanations that are limited in scope, applying to fairly narrow range of phenomena. The term  law  is sometimes used to refer to an idea about how observable phenomena are related — but the term is also used in other ways within science. Theories are deep explanations that apply to a broad range of phenomena and that may integrate many hypotheses and laws. To learn more about this, visit our page on  the different levels of explanation in science .

MISCONCEPTION: Scientific ideas are judged democratically based on popularity.

When newspapers make statements like, “most scientists agree that human activity is the culprit behind global warming,” it’s easy to imagine that scientists hold an annual caucus and vote for their favorite hypotheses. But of course, that’s not quite how it works. Scientific ideas are judged not by their popularity, but on the basis of the evidence supporting or contradicting them. A hypothesis or theory comes to be accepted by many scientists (usually over the course of several years — or decades!) once it has garnered many lines of supporting evidence and has stood up to the scrutiny of the scientific community. A hypothesis accepted by “most scientists,” may not be “liked” or have positive repercussions, but it is one that science has judged likely to be accurate based on the evidence. To learn more about  how science judges ideas , visit our series of pages on the topic in our section on how science works.

MISCONCEPTION: The job of a scientist is to find support for his or her hypotheses.

This misconception likely stems from introductory science labs, with their emphasis on getting the “right” answer and with congratulations handed out for having the “correct” hypothesis all along. In fact, science gains as much from figuring out which hypotheses are likely to be wrong as it does from figuring out which are supported by the evidence. Scientists may have personal favorite hypotheses, but they strive to consider multiple hypotheses and be unbiased when evaluating them against the evidence. A scientist who finds evidence contradicting a favorite hypothesis may be surprised and probably disappointed, but can rest easy knowing that he or she has made a valuable contribution to science.

MISCONCEPTION: Scientists are judged on the basis of how many correct hypotheses they propose (i.e., good scientists are the ones who are "right" most often).

The scientific community  does  value individuals who have good intuition and think up creative explanations that turn out to be correct — but it  also  values scientists who are able to think up creative ways to test a new idea (even if the test ends up contradicting the idea) and who spot the fatal flaw in a particular argument or test. In science, gathering evidence to determine the accuracy of an explanation is just as important as coming up with the explanation that winds up being supported by the evidence.

MISCONCEPTION: Investigations that don't reach a firm conclusion are useless and unpublishable.

Perhaps because the last step of the Scientific Method is usually “draw a conclusion,” it’s easy to imagine that studies that don’t reach a clear conclusion must not be scientific or important. In fact,  most  scientific studies don’t reach “firm” conclusions. Scientific articles usually end with a discussion of the limitations of the tests performed and the alternative hypotheses that might account for the phenomenon. That’s the nature of scientific knowledge — it’s inherently tentative and could be overturned if new evidence, new interpretations, or a better explanation come along. In science, studies that carefully analyze the strengths and weaknesses of the test performed and of the different alternative explanations are particularly valuable since they encourage others to more thoroughly scrutinize the ideas and evidence and to develop new ways to test the ideas. To learn more about publishing and scrutiny in science, visit our discussion of  peer review .

MISCONCEPTION: Scientists are completely objective in their evaluation of scientific ideas and evidence.

Scientists do strive to be unbiased as they consider different scientific ideas, but scientists are people too. They have different personal beliefs and goals — and may favor different hypotheses for different reasons. Individual scientists may not be completely objective, but science can overcome this hurdle through the action of the scientific community, which scrutinizes scientific work and helps balance biases. To learn more, visit  Scientific scrutiny  in our section on the social side of science.

MISCONCEPTION: Scientists' personal traits, experiences, emotions, and values don't factor into the process of science.

Scientists’ personal traits, experiences, emotions, and values influence their selection of research topic, hypotheses, chosen research methods, and interpretations of results and evidence, shaping the course of science in many ways. For example, a social scientist who has experienced poverty might be more likely to study this topic and might formulate different hypotheses about its causes than someone from a different background. Furthermore, experiencing curiosity and wonder is a key motivation for many scientists to pursue their work. Because science is a human endeavor, these fundamentally human traits (our unique identities, emotions, and values) play their role in the process. This means that scientists cannot be completely objective (see above). However, individual biases can be overcome through community scrutiny, helping science self-correct and continue to build more and more accurate explanations for how the world works.

MISCONCEPTION: Science is pure. Scientists work without considering the applications of their ideas.

It’s true that some scientific research is performed without any attention to its applications, but this is certainly not true of all science. Many scientists choose specific areas of research (e.g., malaria genetics) because of the practical ramifications new knowledge in these areas might have. And often, basic research that is performed without any aim toward potential applications later winds up being extremely useful. To learn about some of the many applications of scientific knowledge visit  What has science done for you lately?

Misunderstandings of the limits of science

Misconception: science contradicts the existence of god..

Because of some vocal individuals (both inside and outside of science) stridently declaring their beliefs, it’s easy to get the impression that science and religion are at war. In fact, people of many different faiths and levels of scientific expertise see no contradiction at all between science and religion. Because science deals only with  natural  phenomena and explanations, it cannot support or contradict the existence of  supernatural  entities — like God. To learn more, visit our side trip  Science and religion: Reconcilable differences .

MISCONCEPTION: Science and technology can solve all our problems.

The feats accomplished through the application of scientific knowledge are truly astounding. Science has helped us eradicate deadly diseases, communicate with people all over the world, and build  technologies  that make our lives easier everyday. But for all scientific innovations, the costs must be carefully weighed against the benefits. And, of course, there’s no guarantee that solutions for some problems (e.g., finding an HIV vaccine) exist — though science is likely to help us discover them if they do exist. Furthermore, some important human concerns (e.g. some spiritual and aesthetic questions) cannot be addressed by science at all. Science is a marvelous tool for helping us understand the natural world, but it is not a cure-all for whatever problems we encounter.

Misleading stereotypes of scientists

Misconception: science is a solitary pursuit..

When scientists are portrayed in movies and television shows, they are often ensconced in silent laboratories, alone with their bubbling test-tubes. This can make science seem isolating. In fact, many scientists work in busy labs or field stations, surrounded by other scientists and students. Scientists often collaborate on studies with one another, mentor less experienced scientists, and just chat about their work over coffee. Even the rare scientist who works entirely alone depends on interactions with the rest of the scientific community to scrutinize his or her work and get ideas for new studies. Science is a social endeavor. To learn more, visit our section on the  Social side of science .

MISCONCEPTION: Science is done by "old, white men."

While it is true that Western science used to be the domain of white males, this is no longer the case. The diversity of the scientific community is expanding rapidly. Science is open to anyone who is curious about the natural world and who wants to take a scientific approach to his or her investigations. To see how science benefits from a diverse community, visit  Diversity makes the difference .

MISCONCEPTION: Scientists are atheists.

This is far from true. A 2005 survey of scientists at top research universities found that more than 48% had a religious affiliation and that more than 75% believed that religions convey important truths. 1  Some scientists are not religious, but many others subscribe to a specific faith and/or believe in higher powers. Science itself is a secular pursuit, but welcomes participants from all religious faiths. To learn more, visit our side trip  Science and religion: Reconcilable differences .

Vocabulary mix-ups

Some misconceptions occur simply because scientific language and everyday language use some of the same words differently.

Facts  are statements that we know to be true through direct  observation . In everyday usage, facts are a highly valued form of knowledge because we can be so confident in them. Scientific thinking, however, recognizes that, though facts are important, we can only be completely confident about relatively simple statements. For example, it may be a fact that there are three trees in your backyard. However, our knowledge of how all trees are related to one another is not a fact; it is a complex body of knowledge based on many different  lines of evidence  and reasoning that may change as new  evidence  is discovered and as old evidence is interpreted in new ways. Though our knowledge of tree relationships is not a fact, it is broadly applicable, useful in many situations, and synthesizes many individual facts into a broader framework.  Science  values facts but recognizes that many forms of knowledge are more powerful than simple facts.

In everyday language, a  law  is a rule that must be abided or something that can be relied upon to occur in a particular situation. Scientific laws, on the other hand, are less rigid. They may have exceptions, and, like other scientific knowledge, may be modified or rejected based on new evidence and perspectives. In science, the term  law  usually refers to a generalization about  data  and is a compact way of describing what we’d expect to happen in a particular situation. Some laws are non-mechanistic statements about the relationship among observable phenomena. For example, the ideal gas law describes how the pressure, volume, and temperature of a particular amount of gas are related to one another. It does not describe how gases  must  behave; we know that gases do not precisely conform to the ideal gas law. Other laws deal with phenomena that are not directly observable. For example, the second law of thermodynamics deals with entropy, which is not directly observable in the same way that volume and pressure are. Still other laws offer more mechanistic explanations of phenomena. For example, Mendel’s first law offers a  model  of how genes are distributed to gametes and offspring that helps us make  predictions  about the outcomes of genetic crosses. The term  law  may be used to describe many different forms of scientific knowledge, and whether or not a particular idea is called a law has much to do with its discipline and the time period in which it was first developed.

Observation

In everyday language, the word  observation  generally means something that we’ve seen with our own eyes. In science, the term is used more broadly. Scientific observations can be made directly with our own senses or may be made indirectly through the use of tools like thermometers, pH test kits, Geiger counters, etc. We can’t actually  see  beta particles, but we can observe them using a Geiger counter. To learn more about the role of observation in science, visit  Observation beyond our eyes  in our section on how science works.

In everyday language, the word  hypothesis  usually refers to an educated guess — or an idea that we are quite uncertain about. Scientific hypotheses, however, are much more informed than any guess and are usually based on prior experience, scientific background knowledge, preliminary observations, and logic. In addition, hypotheses are often supported by many different lines of evidence — in which case, scientists are more confident in them than they would be in any mere “guess.” To further complicate matters, science textbooks frequently misuse the term in a slightly different way. They may ask students to make a  hypothesis  about the outcome of an experiment (e.g., table salt will dissolve in water more quickly than rock salt will). This is simply a prediction or a guess (even if a well-informed one) about the outcome of an experiment. Scientific hypotheses, on the other hand, have explanatory power — they are explanations for phenomena. The idea that table salt dissolves faster than rock salt is not very hypothesis-like because it is not very explanatory. A more scientific (i.e., more explanatory) hypothesis might be “The amount of surface area a substance has affects how quickly it can dissolve. More surface area means a faster rate of dissolution.” This hypothesis has some explanatory power — it gives us an idea of  why  a particular phenomenon occurs — and it is testable because it generates expectations about what we should observe in different situations. If the hypothesis is accurate, then we’d expect that, for example, sugar processed to a powder should dissolve more quickly than granular sugar. Students could examine rates of dissolution of many different substances in powdered, granular, and pellet form to further test the idea. The statement “Table salt will dissolve in water more quickly than rock salt” is not a hypothesis, but an expectation generated by a hypothesis. Textbooks and science labs can lead to confusions about the difference between a hypothesis and an expectation regarding the outcome of a scientific test. To learn more about scientific hypotheses, visit  Science at multiple levels  in our section on how science works.

In everyday language, the word  theory  is often used to mean a hunch with little evidential support. Scientific theories, on the other hand, are broad explanations for a wide range of phenomena. They are concise (i.e., generally don’t have a long list of exceptions and special rules), coherent, systematic, and can be used to make predictions about many different sorts of situations. A theory is most  acceptable  to the scientific community when it is strongly supported by many different lines of evidence — but even theories may be modified or overturned if warranted by new evidence and perspectives. To learn more about scientific theories, visit  Science at multiple levels  in our section on how science works.

Falsifiable

The word  falsifiable  isn’t used much in everyday language, but when it is, it is often applied to ideas that have been shown to be untrue. When that’s the case — when an idea has been shown to be false — a scientist would say that it has been falsified. A falsifi able  idea, on the other hand, is one for which there is a conceivable  test  that might produce evidence proving the idea false. Scientists and others influenced by the ideas of the philosopher Karl Popper sometimes assert that only falsifiable ideas are scientific. However, we now recognize that science cannot once-and-for-all prove any idea to be false (or true for that matter). Furthermore, it’s clear that evidence can play a role in supporting particular ideas over others — not just in ruling some ideas out, as implied by the falsifiability criterion. When a scientist says  falsifiable , he or she probably actually means something like  testable , the term we use in this website to avoid confusion. A testable idea is one about which we could gather evidence to help determine whether or not the idea is accurate.

Uncertainty

In everyday language,  uncertainty  suggests the state of being unsure of something. Scientists, however, usually use the word when referring to measurements. The uncertainty of a measurement (not to be confused with the inherent provisionality of all scientific ideas!) is the range of values within which the true value is likely to fall. In science, uncertainty is not a bad thing; it’s simply a fact of life. Every measurement has some uncertainty. If you measure the length of a pen with a standard ruler, you won’t be able to tell whether its length is 5.880 inches, 5.875 inches, or 5.870 inches. A ruler with more precision will help narrow that range, but cannot eliminate uncertainty entirely. For more on a related idea, see our discussion of  error  below.

In everyday language, an error is simply a mistake, but in science, error has a precise statistical meaning. An error is the difference between a measurement and the true value, often resulting from taking a  sample . For example, imagine that you want to know if corn plants produce more massive ears when grown with a new fertilizer, and so you weigh ears of corn from those plants. You take the mass of your sample of 50 ears of corn and calculate an average. That average is a good estimate of what you are really interested in: the average mass of  all  ears of corn that could be grown with this fertilizer. Your estimate is not a mistake — but it does have an error (in the statistical sense of the word) since your estimate is not the true value. Sampling error of the sort described above is inherent whenever a smaller sample is taken to represent a larger entity. Another sort of error results from systematic biases in measurement (e.g., if your scale were calibrated improperly, all of your measurements would be off). Systematic error biases measurements in a particular direction and can be more difficult to quantify than sampling error.

In everyday language,  prediction  generally refers to something that a fortune teller makes about the future. In science, the term  prediction  generally means “what we would expect to happen or what we would expect to observe if this idea were accurate.” Sometimes, these scientific predictions have nothing at all to do with the future. For example, scientists have hypothesized that a huge asteroid struck the Earth 4.5 billion years ago, flinging off debris that formed the moon. If this idea were true, we would  predict  that the moon today would have a similar composition to that of the Earth’s crust 4.5 billion years ago — a prediction which does seem to be accurate. This hypothesis deals with the deep history of our solar system and yet it involves predictions — in the scientific sense of the word. Ironically, scientific predictions often have to do with past events. In this website, we’ve tried to reduce confusion by using the words  expect and  expectation  instead of  predict  and  prediction . To learn more, visit  Predicting the past  in our section on the core of science.

Belief/believe

When we, in everyday language, say that we believe in something, we may mean many things — that we support a cause, that we have faith in an idea, or that we think something is accurate. The word  belief  is often associated with ideas about which we have strong convictions, regardless of the evidence for or against them. This can generate confusion when a scientist claims to “believe in” a scientific hypothesis or theory. In fact, the scientist probably means that he or she “ accepts ” the idea — in other words, that he or she thinks the scientific idea is the most accurate available based on a critical evaluation of the evidence. Scientific ideas should always be accepted or rejected based on the evidence for or against them — not based on faith, dogma, or personal conviction.

Roadblocks to learning science

In school, many students get the wrong impression of science. While not technically misconceptions, these overgeneralizations are almost always inaccurate — and can make it more difficult for the students who hold them to learn science.

MISCONCEPTION: Science is boring.

  Memorizing facts from a textbook can be boring — but science is much more than the knowledge that makes its way into school books. Science is an ongoing and unfinished process of discovery. Some scientists travel all over the world for their research. Others set up experiments that no one has ever tried before. And all scientists are engaged in a thrilling quest — to learn something brand new about the natural world. Some parts of scientific training or investigations may be tedious, but science itself is exciting! To see how a scientific perspective can make the world a more exciting and intriguing place, visit our side trip  Think science .

MISCONCEPTION: Science isn't important in my life.

It’s easy to think that what scientists do in far-off laboratories and field stations has little relevance to your everyday life — after all, not many of us deal with super colliders or arctic plankton on a regular basis — but take another look around you. All the technologies, medical advances, and knowledge that improve our lives everyday are partly the result of scientific research. Furthermore, the choices you make when you vote in elections and support particular causes can influence the course of science. Science is deeply interwoven with our everyday lives. To see how society influences science, visit  Science and society . To learn more about how scientific advances affect your life, visit  What has science done for you lately?

MISCONCEPTION: I am not good at science.

Some students find science class difficult — but this doesn’t translate to not being good at science. First of all, school science can be very different from real science. The background knowledge that one learns in school is important for practicing scientists, but it is only part of the picture. Scientific research also involves creative problem-solving, communicating with others, logical reasoning, and many other skills that might or might not be a part of every science class. Second, science encompasses a remarkably broad set of activities. So maybe you don’t care much for the periodic table — but that doesn’t mean that you wouldn’t be great at observing wild chimpanzee behavior, building computer models of tectonic plate movement, or giving talks about psychology experiments at scientific meetings. Often when a student claims to “not be good at science,” it really just means that he or she hasn’t yet found a part of science that clicks with his or her interests and talents.

1 Ecklund, E.H., and C.P. Scheitle. 2007. Religion among academic scientists: Distinctions, disciplines, and demographics.  Social Problems  54(2):289-307.

• Teaching resources
• Unfortunately, many textbooks promulgate misconceptions about the nature and process of science. Use this list to review your textbook, and then discuss any misrepresentations with students.
• You can highlight misconceptions about science that are promulgated in the media by starting a bulletin board that highlights examples of misconceptions found in the popular press — for example, misuses of the word theory, implications that scientists always use “the scientific method,” or that experimental science is more rigorous than non-experimental science.
• Use word lists to combat misconceptions about science that stem from vocabulary mix-ups. Find out how in this article distributed with permission from Science Scope.

Alignment with science standards

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