a good hypothesis is falsifiable

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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 18, 2024 from Explorable.com: https://explorable.com/falsifiability

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5 Characteristics of a Good Hypothesis: A Guide for Researchers

• by Brian Thomas
• October 10, 2023

Are you a curious soul, always seeking answers to the whys and hows of the world? As a researcher, formulating a hypothesis is a crucial first step towards unraveling the mysteries of your study. A well-crafted hypothesis not only guides your research but also lays the foundation for drawing valid conclusions. But what exactly makes a hypothesis a good one? In this blog post, we will explore the five key characteristics of a good hypothesis that every researcher should know.

Here, we will delve into the world of hypotheses, covering everything from their types in research to understanding if they can be proven true. Whether you’re a seasoned researcher or just starting out, this blog post will provide valuable insights on how to craft a sound hypothesis for your study. So let’s dive in and uncover the secrets to formulating a hypothesis that stands strong amidst the scientific rigor!

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5 Characteristics of a Good Hypothesis

Clear and specific.

A good hypothesis is like a GPS that guides you to the right destination. It needs to be clear and specific so that you know exactly what you’re testing. Avoid vague statements or general ideas. Instead, focus on crafting a hypothesis that clearly states the relationship between variables and the expected outcome. Clarity is key, my friend!

Testable and Falsifiable

A hypothesis might sound great in theory, but if you can’t test it or prove it wrong, then it’s like chasing unicorns. A good hypothesis should be testable and falsifiable – meaning there should be a way to gather evidence to support or refute it. Don’t be afraid to challenge your hypothesis and put it to the test. Only when it can be proven false can it truly be considered a good hypothesis.

Based on Existing Knowledge

Imagine trying to build a Lego tower without any Lego bricks. That’s what it’s like to come up with a hypothesis that has no basis in existing knowledge. A good hypothesis is grounded in previous research, theories, or observations. It shows that you’ve done your homework and understand the current state of knowledge in your field. So, put on your research hat and gather those building blocks for a solid hypothesis!

Specific Predictions

No, we’re not talking about crystal ball predictions or psychic abilities here. A good hypothesis includes specific predictions about what you expect to happen. It’s like making an educated guess based on your understanding of the variables involved. These predictions help guide your research and give you something concrete to look for. So, put on those prediction goggles, my friend, and let’s get specific!

Relevant to the Research Question

A hypothesis is a road sign that points you in the right direction. But if it’s not relevant to your research question, then you might end up in a never-ending detour. A good hypothesis aligns with your research question and addresses the specific problem or phenomenon you’re investigating. Keep your focus on the main topic and avoid getting sidetracked by shiny distractions. Stay relevant, my friend, and you’ll find the answers you seek!

And there you have it: the five characteristics of a good hypothesis. Remember, a good hypothesis is clear, testable, based on existing knowledge, makes specific predictions, and is relevant to your research question. So go forth, my friend, and hypothesize your way to scientific discovery!

FAQs: Characteristics of a Good Hypothesis

In the realm of scientific research, a hypothesis plays a crucial role in formulating and testing ideas. A good hypothesis serves as the foundation for an experiment or study, guiding the researcher towards meaningful results. In this FAQ-style subsection, we’ll explore the characteristics of a good hypothesis, their types, formulation, and more. So let’s dive in and unravel the mysteries of hypothesis-making!

What Are Two Important Characteristics of a Good Hypothesis

A good hypothesis possesses two important characteristics:

Testability : A hypothesis must be testable to determine its validity. It should be formulated in a way that allows researchers to design and conduct experiments or gather data for analysis. For example, if we hypothesize that “drinking herbal tea reduces stress,” we can easily test it by conducting a study with a control group and a group drinking herbal tea.

Falsifiability : Falsifiability refers to the potential for a hypothesis to be proven wrong. A good hypothesis should make specific predictions that can be refuted or supported by evidence. This characteristic ensures that hypotheses are based on empirical observations rather than personal opinions. For instance, the hypothesis “all swans are white” can be falsified by discovering a single black swan.

What Are the Types of Hypothesis in Research

In research, there are three main types of hypotheses:

Null Hypothesis (H0) : The null hypothesis is a statement of no effect or relationship. It assumes that there is no significant difference between variables or no effect of a treatment. Researchers aim to reject the null hypothesis in favor of an alternative hypothesis.

Alternative Hypothesis (HA or H1) : The alternative hypothesis is the opposite of the null hypothesis. It asserts that there is a significant difference between variables or an effect of a treatment. Researchers seek evidence to support the alternative hypothesis.

Directional Hypothesis : A directional hypothesis predicts the specific direction of the relationship or difference between variables. For example, “increasing exercise duration will lead to greater weight loss.”

Can a Hypothesis Be Proven True

In scientific research, hypotheses are not proven true; they are supported or rejected based on empirical evidence . Even if a hypothesis is supported by multiple studies, new evidence could arise that contradicts it. Scientific knowledge is always subject to revision and refinement. Therefore, the goal is to gather enough evidence to either support or reject a hypothesis, rather than proving it absolutely true.

What Are the Six Parts of a Hypothesis

A hypothesis typically consists of six essential parts:

Research Question : A clear and concise question that the hypothesis seeks to answer.

Variables : Identification of the independent (manipulated) and dependent (measured) variables involved in the hypothesis.

Population : The specific group or individuals the hypothesis is concerned with.

Relationship or Comparison : The expected relationship or difference between variables, often indicated by directional terms like “more,” “less,” “higher,” or “lower.”

Predictability : A statement of the predicted outcome or result based on the relationship between variables.

Testability : The ability to design an experiment or gather data to support or reject the hypothesis.

How Do You Start a Hypothesis Sentence

When starting a hypothesis sentence, it is essential to use clear and concise language to express your ideas. A common approach is to use the phrase “If…then…” to establish the conditional relationship between variables. For example:

• If [independent variable], then [dependent variable] because [explanation of expected relationship].

This structure allows for a straightforward and logical formulation of the hypothesis.

What Are Examples of Hypotheses

Here are a few examples of well-formulated hypotheses:

If exposure to sunlight increases, then plants will grow taller because sunlight is necessary for photosynthesis.

If students receive praise for good grades, then their motivation to excel will increase because they seek recognition and approval.

If the dose of a painkiller is increased, then the relief from pain will last longer because a higher dosage has a prolonged effect.

What Are the Five Key Elements to a Good Hypothesis

A good hypothesis should include the following five key elements:

Clarity : The hypothesis should be clear and specific, leaving no room for interpretation.

Testability : It should be possible to test the hypothesis through experimentation or data collection.

Relevance : The hypothesis should be directly tied to the research question or problem being investigated.

Specificity : It must clearly state the relationship or difference between variables being studied.

Falsifiability : The hypothesis should make predictions that can be refuted or supported by empirical evidence.

What Makes a Good Hypothesis in a Research Paper

In a research paper, a good hypothesis should have the following characteristics:

Relevance : It must directly relate to the research topic and address the objectives of the study.

Clarity : The hypothesis should be concise and precisely worded to avoid confusion.

Unambiguous : It must leave no room for multiple interpretations or ambiguity.

Logic : The hypothesis should be based on rational and logical reasoning, considering existing theories and observations.

Empirical Support : Ideally, the hypothesis should be supported by prior empirical evidence or strong theoretical justifications.

Is a Hypothesis Always a Question

No, a hypothesis is not always in the form of a question. While some hypotheses can take the form of a question, others may be statements asserting a relationship or difference between variables. The form of a hypothesis depends on the research question being addressed and the researcher’s preferred style of expression.

What Are the Three Things Needed for a Good Hypothesis

For a hypothesis to be considered good, it must fulfill the following three criteria:

Testability : The hypothesis should be formulated in a way that allows for empirical testing through experimentation or data collection.

Falsifiability : It must make specific predictions that can be potentially refuted or supported by evidence.

Relevance : The hypothesis should directly address the research question or problem being investigated.

What Are the Four Components to a Good Hypothesis

A good hypothesis typically consists of four components:

Independent Variable : The variable being manipulated or controlled by the researcher.

Dependent Variable : The variable being measured or observed to determine the effect of the independent variable.

Directionality : The predicted relationship or difference between the independent and dependent variables.

Population : The specific group or individuals to which the hypothesis applies.

How Do You Formulate a Hypothesis

To formulate a hypothesis, follow these steps:

Identify the Research Topic : Clearly define the area or phenomenon you want to study.

Conduct Background Research : Review existing literature and research to gain knowledge about the topic.

Formulate a Research Question : Ask a clear and focused question that you want to answer through your hypothesis.

State the Null and Alternative Hypotheses : Develop a null hypothesis to assume no effect or relationship, and an alternative hypothesis to propose a significant effect or relationship.

Decide on Variables and Relationships : Determine the independent and dependent variables and the predicted relationship between them.

Refine and Test : Refine your hypothesis, ensuring it is clear, testable, and falsifiable. Then, design experiments or gather data to support or reject it.

What Is a Characteristic of a Hypothesis MCQ

Multiple-choice questions (MCQ) regarding the characteristics of a hypothesis often assess knowledge on the testability and falsifiability of hypotheses. They may ask about the criteria that distinguish a good hypothesis from a poor one or the importance of making specific predictions. Remember to choose answers that emphasize the empirical and testable nature of hypotheses.

What Five Criteria Must Be Satisfied for a Hypothesis to Be Scientific

For a hypothesis to be considered scientific, it must satisfy the following five criteria:

Testability : The hypothesis must be formulated in a way that allows it to be tested through experimentation or data collection.

Falsifiability : It should make specific predictions that can be potentially refuted or supported by empirical evidence.

Empirical Basis : The hypothesis should be based on empirical observations or existing theories and knowledge.

Relevance : It must directly address the research question or problem being investigated.

Objective : A scientific hypothesis should be free from personal biases or subjective opinions, focusing on objective observations and analysis.

What Are the Steps of Theory Development in Scientific Methods

In scientific methods, theory development typically involves the following steps:

Observation : Identifying a phenomenon or pattern worthy of investigation through observation or empirical data.

Formulation of a Hypothesis : Constructing a hypothesis that explains the observed phenomena or predicts a relationship between variables.

Data Collection : Gathering relevant data through experiments, surveys, observations, or other research methods.

Analysis : Analyzing the collected data to evaluate the hypothesis’s predictions and determine their validity.

Revision and Refinement : Based on the analysis, refining the hypothesis, modifying the theory, or formulating new hypotheses for further investigation.

Which of the Following Makes a Good Hypothesis

A good hypothesis is characterized by:

Testability : The ability to form experiments or gather data to support or refute the hypothesis.

Falsifiability : The potential for the hypothesis’s predictions to be proven wrong based on empirical evidence.

Clarity : A clear and concise statement or question that leaves no room for ambiguity.

Relevancy : Directly addressing the research question or problem at hand.

Remember, it is important to select the option that encompasses all these characteristics.

What Are the Characteristics of a Good Hypothesis

A good hypothesis possesses several characteristics, such as:

Testability : It should allow for empirical testing through experiments or data collection.

Falsifiability : The hypothesis should make specific predictions that can be potentially refuted or supported by evidence.

Clarity : It must be clearly and precisely formulated, leaving no room for ambiguity or multiple interpretations.

Relevance : The hypothesis should directly relate to the research question or problem being investigated.

What Is the Five-Step p-value Approach to Hypothesis Testing

The five-step p-value approach is a commonly used framework for hypothesis testing:

Step 1: Formulating the Hypotheses : The null hypothesis (H0) assumes no effect or relationship, while the alternative hypothesis (HA) proposes a significant effect or relationship.

Step 2: Setting the Significance Level : Decide on the level of significance (α), which represents the probability of rejecting the null hypothesis when it is true. The commonly used level is 0.05 (5%).

Step 3: Collecting Data and Performing the Test : Acquire and analyze the data, calculating the test statistic and the corresponding p-value.

Step 4: Comparing the p-value with the Significance Level : If the p-value is less than the significance level (α), reject the null hypothesis. Otherwise, fail to reject the null hypothesis.

Step 5: Drawing Conclusions : Based on the comparison in Step 4, interpret the results and draw conclusions about the hypothesis.

What Are the Stages of Hypothesis

The stages of hypothesis generally include:

Observation : Identifying a pattern, phenomenon, or research question that warrants investigation.

Formulation : Developing a hypothesis that explains or predicts the relationship or difference between variables.

Testing : Collecting data, designing experiments, or conducting studies to gather evidence supporting or refuting the hypothesis.

Analysis : Assessing the collected data to determine whether the results support or reject the hypothesis.

Conclusion : Drawing conclusions based on the analysis and making further iterations, refinements, or new hypotheses for future research.

What Is a Characteristic of a Good Hypothesis

A characteristic of a good hypothesis is its ability to make specific predictions about the relationship or difference between variables. Good hypotheses avoid vague statements and clearly articulate the expected outcomes. By doing so, researchers can design experiments or gather data that directly test the predictions, leading to meaningful results.

How Do You Write a Good Hypothesis Example

To write a good hypothesis example, follow these guidelines:

If possible, use the “If…then…” format to express a conditional relationship between variables.

Be clear and concise in stating the variables involved, the predicted relationship, and the expected outcome.

Ensure the hypothesis is testable, meaning it can be evaluated through experiments or data collection.

For instance, consider the following example:

If students study for longer periods of time, then their test scores will improve because increased study time allows for better retention of information and increased proficiency.

What Is the Difference Between Hypothesis and Hypotheses

The main difference between a hypothesis and hypotheses lies in their grammatical number. A hypothesis refers to a single statement or proposition that is formulated to explain or predict the relationship between variables. On the other hand, hypotheses is the plural form of the term hypothesis, commonly used when multiple statements or propositions are proposed and tested simultaneously.

What Is a Good Hypothesis Statement

A good hypothesis statement exhibits the following qualities:

Clarity : It is written in clear and concise language, leaving no room for confusion or ambiguity.

Testability : The hypothesis should be formulated in a way that enables testing through experiments or data collection.

Specificity : It must clearly state the predicted relationship or difference between variables.

By adhering to these criteria, a good hypothesis statement guides research efforts effectively.

What Is Not a Characteristic of a Good Hypothesis

A characteristic that does not align with a good hypothesis is subjectivity . A hypothesis should be objective, based on empirical observations or existing theories, and free from personal bias. While personal interpretations and opinions can inspire the formulation of a hypothesis, it must ultimately rely on objective observations and be open to empirical testing.

By now, you’ve gained insights into the characteristics of a good hypothesis, including testability, falsifiability, clarity,

• characteristics
• falsifiable
• good hypothesis
• hypothesis testing
• null hypothesis
• observations
• scientific rigor

Brian Thomas

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What does it mean for science to be falsifiable?

Posted on July 31, 2021 by Evan Arnet

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Edited by Jennifer Sieben and Joe Vuletich

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

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

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

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criterion of falsifiability

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• Simply Psychology - Karl Popper: Theory of Falsification
• Academia - Is Popper’s Criterion of Falsifiability a Solution to the Problem of Demarcation?

criterion of falsifiability , in the philosophy of science , a standard of evaluation of putatively scientific theories, according to which a theory is genuinely scientific only if it is possible in principle to establish that it is false. The British philosopher Sir Karl Popper (1902–94) proposed the criterion as a foundational method of the empirical sciences. He held that genuinely scientific theories are never finally confirmed, because disconfirming observations (observations that are inconsistent with the empirical predictions of the theory) are always possible no matter how many confirming observations have been made. Scientific theories are instead incrementally corroborated through the absence of disconfirming evidence in a number of well-designed experiments. According to Popper, some disciplines that have claimed scientific validity—e.g., astrology , metaphysics , Marxism , and psychoanalysis —are not empirical sciences, because their subject matter cannot be falsified in this manner.

2.4 Developing a Hypothesis

Learning objectives.

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

Theories and Hypotheses

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

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

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

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

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

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

Theory Testing

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

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

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

Incorporating Theory into Your Research

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

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

Characteristics of a Good Hypothesis

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

Key Takeaways

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

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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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Law of Falsifiability

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

Examples of Law of Falsifiability

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

Why is it Important?

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

Implications and Applications

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

Comparison with Related Axioms

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

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

Controversies

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

Related Topics

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

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

Karl Popper: Theory of Falsification

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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

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

Theory of Falsification

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

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

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

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

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

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

Popper gives the following example:

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

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

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

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

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

Critical Evaluation

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

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

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

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

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

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

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

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

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

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

Further Information

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

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High school biology (DEPRECATED)

Course: high school biology (deprecated)   >   unit 1.

• Biology overview
• Preparing to study biology
• What is life?
• The scientific method
• Data to justify experimental claims examples
• Scientific method and data analysis
• Introduction to experimental design
• Controlled experiments

Biology and the scientific method review

• Experimental design and bias

The nature of biology

Properties of life.

• Organization: Living things are highly organized (meaning they contain specialized, coordinated parts) and are made up of one or more cells .
• Metabolism: Living things must use energy and consume nutrients to carry out the chemical reactions that sustain life. The sum total of the biochemical reactions occurring in an organism is called its metabolism .
• Homeostasis : Living organisms regulate their internal environment to maintain the relatively narrow range of conditions needed for cell function.
• Growth : Living organisms undergo regulated growth. Individual cells become larger in size, and multicellular organisms accumulate many cells through cell division.
• Reproduction : Living organisms can reproduce themselves to create new organisms.
• Response : Living organisms respond to stimuli or changes in their environment.
• Evolution : Populations of living organisms can undergo evolution , meaning that the genetic makeup of a population may change over time.

Scientific methodology

Scientific method example: failure to toast, experimental design, reducing errors and bias.

• Having a large sample size in the experiment: This helps to account for any small differences among the test subjects that may provide unexpected results.
• Repeating experimental trials multiple times: Errors may result from slight differences in test subjects, or mistakes in methodology or data collection. Repeating trials helps reduce those effects.
• Including all data points: Sometimes it is tempting to throw away data points that are inconsistent with the proposed hypothesis. However, this makes for an inaccurate study! All data points need to be included, whether they support the hypothesis or not.
• Using placebos , when appropriate: Placebos prevent the test subjects from knowing whether they received a real therapeutic substance. This helps researchers determine whether a substance has a true effect.
• Implementing double-blind studies , when appropriate: Double-blind studies prevent researchers from knowing the status of a particular participant. This helps eliminate observer bias.

Communicating findings

Things to remember.

• A hypothesis is not necessarily the right explanation. Instead, it is a possible explanation that can be tested to see if it is likely correct, or if a new hypothesis needs to be made.
• Not all explanations can be considered a hypothesis. A hypothesis must be testable and falsifiable in order to be valid. For example, “The universe is beautiful" is not a good hypothesis, because there is no experiment that could test this statement and show it to be false.
• In most cases, the scientific method is an iterative process. In other words, it's a cycle rather than a straight line. The result of one experiment often becomes feedback that raises questions for more experimentation.
• Scientists use the word "theory" in a very different way than non-scientists. When many people say "I have a theory," they really mean "I have a guess." Scientific theories, on the other hand, are well-tested and highly reliable scientific explanations of natural phenomena. They unify many repeated observations and data collected from lots of experiments.

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What Is a Testable Hypothesis?

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

Requirements for a Testable Hypothesis

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

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

Examples of a Testable Hypothesis

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

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

Examples of a Hypothesis Not Written in a Testable Form

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

How to Propose a Testable Hypothesis

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

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

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Is the idea that a scientific hypothesis must be falsifiable obsolete.

In many science-related circles (atheist and skeptic communities, professional scientists) it is often taken for granted that the main criterion of what constitutes a scientific hypothesis/statement is falsifiability : it doesn't have to be verifiable but it must be falsifiable.

For example, some otherwise reasonable people with this quite pervasive view insist that " There is alien life on other planets " is not a scientific hypothesis, because it is not falsifiable in any sensible way (EDIT: why? See below). You can probably tell from my phrasing that I completely disagree.

Would it be fair to call this view obsolete in philosophy of science?

UPDATE: As many have completely fairly pointed out (referencing the Duhem-Quine Thesis), we can never completely falsify a statement because of auxiliary/background assumptions and other reasons. But my hypothetical interlocutor, perhaps from one of the above-mentioned scientifically minded communities, can still rescue the view. They can say:

"Sure, but let's not be nitpicky. By falsifying something let's not mean some sort of idealized 100% inescapable disproof - let's adopt a more realistic criterion of disproving for all practical purposes , or something similar."

For example, " There's no life on other planets" is easily falsifiable in that more realistic sense - just by observing another planet with life, Duhem-Quine Thesis notwithstanding.

But I think there's a more fundamental issue with my interlocutor's view, from which it cannot be rescued. To clarify, the view is something like:

" Scientific statements can't be proven right, only proven wrong, and we can never verify something but only keep falsifying alternatives ." I haven't mentioned Popper in my original post, because I don't want to misrepresent him, but of course this notion, pervasive in the communities I mentioned, is his or closely related to his.

The core of the view seems to be a huge fundamental asymmetry between verification and falsification , specifically that only the latter is possible for scientific statements.

My question then is: is it fair to call the idea of such an asymmetry obsolete? (Even if we construe falsification in a realistic way, to take care of Duhem-Quine)

APPENDIX: The task of thoroughly exploring every planet is physically impossible since the universe is bigger than the observable universe. And even if we limited the statement to be only about planets within the observable universe, the task would take so long that some planets will escape beyond the bounds of the observable universe due to cosmic expansion so we can never explore them.

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• Technical support

falsifiability

• Robert Sheldon
• Ivy Wigmore

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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The Discovery of the Falsifiability Principle

• First Online: 01 January 2023

Cite this chapter

• Friedel Weinert 2  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Weinert, F. (2022). The Discovery of the Falsifiability Principle. In: Karl Popper. Springer Biographies. Springer, Cham. https://doi.org/10.1007/978-3-031-15424-9_3

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You Can Know Things

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 Hypothesis? [Tips with Examples]

Click here if you have ever found yourself in the position of having to wrestle with the development of a hypothesis for your research paper. As an expert writer, I have seen that this is where most students begin to sweat. It is a potpourri of theory and practice, hence rather intimidating. But not to worry because I have got your back. This guide is a pool of tips and tricks for writing a hypothesis to set the stage for compelling research.

What is a Hypothesis?

A hypothesis is a tentative statement, usually in the form of an educated guess, that provides a probable explanation for something either a phenomenon or a relationship between variables. This will, therefore, form a basis for conducting experiments and research studies, hence laying down the course of your investigation and mainly laying the ground for your conclusion.

A good hypothesis should be:

Specific and clear

Testable and falsifiable

Based upon existing knowledge

Logically consistent

Types of Hypothesis

There are different kinds of hypotheses used in research, all of which serve different purposes depending on the nature of the study. Here are eight common types:

1. The null hypothesis (H0):  asserts that there is no effect or relationship between variables. This forms a baseline for comparison. Example: "There is no difference in test scores for students who study music and for those who do not."

2. Alternative Hypothesis (H1): The hypothesis that postulates some effect or relationship between variables; it is, therefore, the opposite of the null hypothesis. For instance, "Students who study with music have different test scores than those who study in silence."

3. Simple Hypothesis: The hypothesis that states a relationship between two variables: one independent and one dependent. For example, "More sunlight increases plant growth."

4. Complex Hypothesis: This hypothesis involves the relationship of more than one variable. For example, "More sunlight and water increase plant growth."

5. Directional Hypothesis: The hypothesis which specifies the direction of the effect between variables. For instance, "Students who study with music will have higher test scores than students who study in silence."

6. Non-Directional Hypothesis: This is a hypothesis used where the relationship is indicated, but the direction is not specified. For example, "There is a difference in test scores between students who study with music and those who study in silence."

7. Associative Hypothesis: This hypothesis merely states that the change in one variable is associated with a change in another. It does not indicate cause and effect. For example: "There is a relationship between study habits and academic performance."

8. Causal Hypothesis: This hypothesis states that one variable causes a change in another. For example: "Increased study time results in higher test scores."

Understanding such types of hypotheses will help in the selection of the correct hypothesis for your research and in making your analysis clear and effective.

5 Steps to Write a Good Hypothesis [With Examples]

An excellent hypothesis provides a backbone to any scientific research. Leave some help behind in writing one? Follow this easy guide:

Step 1: Ask a Question

First, you must understand what your research question is. Suppose you want to carry out an experiment on plant growth. Your question can be, "How does sunlight affect plant growth?"

Use WPS AI to help when you get stuck. Feed it a topic, and it will come up with related questions to ask.

Step 2: Do Preliminary Research

Do some research to see what's already known about your topic. That way, you can build upon existing knowledge.

Research information in journals, books and credible websites. Then summarize what you read. This will help you formulate your hypothesis.

Step 3: Define Variables

Identify your variables:

Independent Variable: What you manipulate. For example, the amount of sun.

Dependent Variable: What you measure. For example, plant growth rate.

Clearly defining these makes your hypothesis specific and testable.

Step 4: State Your Hypothesis

State your question in the form of a hypothesis. Here are some examples:

If  then: "If plants receive more sunlight, then they will grow faster."

Comparative statements: "Plants receiving more sunlight grow faster than plants receiving less."

Correlation statements: "There is positive correlation between sunlight and plant growth." This kind of pattern makes your hypothesis easy to test.

Step 5: Refine Your Hypothesis

Revise your hypothesis to be clear and specific, and elicit feedback to improve it.

You will also need a null hypothesis, which says that there is no effect or relationship between variables. An example would be, "Sunlight has no effect on the growth of plants."

With these steps, you are now bound to come up with a testable hypothesis. WPS AI can help you in this process more efficiently.

Characteristics of a Good Hypothesis

A good hypothesis is seen as the backbone of doing effective research. Following are some key characteristics that define a good hypothesis:

A good hypothesis has to be testable either by experimentation or observation. The hypothesis should clearly predict what can be measured or observed. For example, "If it receives more sunlight, the plant will grow taller" is a testable hypothesis since it states what can be measured.

Falsifiable

A hypothesis has to be falsifiable: it should be able to prove it wrong. This feature is important because it accommodates testing in science. For example, the statement "All swans are white" is falsifiable since it just takes one black swan to disprove the claim.

A good hypothesis should be grounded in current knowledge and should be properly reasoned. It should be broad or reasonable within existing knowledge. For example, "Increasing the amount of sunlight will boost plant growth" makes sense, in that it tallies with generally known facts about photosynthesis.

Specific and Clear

What is needed is clarity and specificity. A hypothesis has to be brief, yet free from ambiguity. For instance, "Increased sunlight leads to taller plants" is clear and specific whereas "Sunlight affects plants" is too vague.

Built upon Prior Knowledge

A good hypothesis is informed by prior research and existing theories. The available knowledge enlightens it to build on what is known to find new relationships or effects. For example, "Given photosynthesis requires sunlight, increasing sunlight will enhance plant growth" is informed by available scientific understanding.

Ethical Considerations

Finally, a good hypothesis needs to consider the ethics involved. The research should not bring damage to participants or the environment. For instance, "How the new drug will affect a human when tested without testing it on animals" may present an ethical concern.

Checklist for Reviewing Your Hypothesis

To be certain that your hypothesis has the following characteristics, use this checklist to review your hypothesis:

1. Is the hypothesis testable through experimentation or observation?

2. Can the hypothesis be proven false?

3. Is the hypothesis logically deduced from known facts?

4. Is your hypothesis clear and specific?

5. Does your hypothesis relate to previous research or theories?

6. Will there be any ethical issues with the proposed research?

7. Are your independent and dependent variables well defined?

8. Is your hypothesis concise and ambiguity free?

9. Did you get feedback to help in refining your hypothesis?

10. Does your hypothesis contain a null hypothesis for comparison?

By making sure that your hypothesis has these qualities, you are much more likely to set yourself on the course of higher-quality research and larger impacts. WPS AI can help fine-tune a hypothesis to ensure it is well-structured and clear.

Using WPS to Perfect your Hypothesis

Drafting a good hypothesis is the real inception of any research project. WPS AI, with its advanced language functions, can very strongly improve this stage of your study. Here's how WPS AI can help you perfect your hypothesis:

Check Grammar and Syntax

Grammar and punctuation errors can make your hypothesis weak. WPS AI checks and corrects this with the assurance that your hypothesis is as clear as possible and professional in its presentation. For example, when your hypothesis is written, "If the temperature increases then plant growth will increases", WPS AI can correct it to "If the temperature increases, then plant growth will increase."

Rewrite Your Hypothesis for Clarity

There needs to be a clear hypothesis. WPS AI can suggest ways to reword your hypothesis so that it makes sense. If your original hypothesis is, "More sunlight will result in more significant plant growth due to photosynthesis," WPS AI can suggest, "Increased sunlight will lead to greater plant growth through enhanced photosynthesis."

Automatic Content Expansion

Sometimes, your hypothesis or the related paragraphs may require more detail. WPS AI's [Continue Writing] feature can help enlarge the content. For example, after having written, "This study will examine the effects of sunlight on plant growth", using [Continue Writing] it can enlarge it to, "This research paper is going to study how sunlight affects the growth of plants by measuring their height and their health under different amounts of sunlight over a period of six weeks."

WPS AI is a great tool that can help you in drafting a good hypothesis for your research. It will help you check grammar, syntax, clarity, and completeness. Using WPS AI , you will be assured that the results of your hypothesis will be well-written and clear to understand.

What is the difference between a hypothesis and a theory?

The hypothesis is one single testable prediction regarding some phenomenon. The theory is an explanation for some part of the natural world which is well-substantiated by a body of evidence, together with multiple hypotheses.

What do I do if my hypothesis isn't supported by my data?

If your results turn out not to support your hypothesis, analyze the data again to see why your result rejects your hypothesis. Do not manipulate the observations or experiment so that it leads to your hypothesis.

Can there be more than one hypothesis in a research study?

Yes, there may be more than one hypothesis, especially when one research study is examining several interrelated phenomena or variables. Each hypothesis has to be separately and clearly stated and tested.

Correct formulation of a strong, testable hypothesis is one of the most critical steps in the application of the scientific method and within academic research. The steps provided in this article will help you write a hypothesis that is clear, specific, and based on available knowledge. Give the tools and tips a try to elevate your academic writing and kick your research up a notch.

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