all experiments must be

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Biology archive

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

• Controlled experiments
• The scientific method and experimental design

Introduction

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

Scientific method example: Failure to toast

1. make an observation., 2. ask a question., 3. propose a hypothesis., 4. make predictions., 5. test the predictions..

• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

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

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

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Scientific Method

Reviewed by: BD Editors

The scientific method is a series of processes that people can use to gather knowledge about the world around them, improve that knowledge, and attempt to explain why and/or how things occur. This method involves making observations, forming questions, making hypotheses, doing an experiment, analyzing the data, and forming a conclusion. Every scientific experiment performed is an example of the scientific method in action, but it is also used by non-scientists in everyday situations.

Scientific Method Overview

The scientific method is a process of trying to get as close as possible to the  objective truth . However, part of the process is to constantly refine your conclusions, ask new questions, and continue the search for the rules of the universe. Through the scientific method, scientists are trying to uncover how the world works and discover the laws that make it function in that way. You can use the scientific method to find answers for almost any question, though the scientific method can yield conflicting evidence based on the method of experimentation. In other words, the scientific method is a very useful way to figure things out – though it must be used with caution and care!

Scientific Method Steps

The exact steps of the scientific method vary from source to source , but the general procedure is the same: acquiring knowledge through observation and testing.

Making an Observation

The first step of the scientific method is to make an observation about the world around you. Before hypotheses can be made or experiments can be done, one must first notice and think about some sort of phenomena occurring. The scientific method is used when one does not know why or how something is occurring and wants to uncover the answer. But, before you can form a question you must notice something puzzling in the first place.

Asking a Question

Next, one must ask a question based on their observations. Here are some examples of good questions:

• Why is this thing occurring?
• How is this thing occurring?
• Why or how does it happen this way?

Sometimes this step is listed first in the scientific method, with making an observation (and researching the phenomena in question) listed as second. In reality, both making observations and asking questions tend to happen around the same time.

One can see a confusing occurrence and immediately think, “why is it occurring?” When observations are being made and questions are being formed, it is important to do research to see if others have already answered the question or uncovered information that may help you shape your question. For example, if you find an answer to why something is occurring, you may want to go a step further and figure out how it occurs.

Forming a Hypothesis

A hypothesis is an educated guess to explain the phenomena occurring based on prior observations. It answers the question posed in the previous step. Hypotheses can be specific or more general depending on the question being asked, but all hypotheses must be testable by gathering evidence that can be measured. If a hypothesis is not testable, then it is impossible to perform an experiment to determine whether the hypothesis is supported by evidence.

Performing an Experiment

After forming a hypothesis, an experiment must be set up and performed to test the hypothesis. An experiment must have an independent variable (something that is manipulated by the person doing the experiment), and a dependent variable (the thing being measured which may be affected by the independent variable). All other variables must be controlled so that they do not affect the outcome. During an experiment, data is collected. Data is a set of values; it may be quantitative (e.g. measured in numbers) or qualitative (a description or generalization of the results).

For example, if you were to test the effect of sunlight on plant growth, the amount of light would be the independent variable (the thing you manipulate) and the height of the plants would be the dependent variable (the thing affected by the independent variable). Other factors such as air temperature, amount of water in the soil, and species of plant would have to be kept the same between all of the plants used in the experiment so that you could truly collect data on whether sunlight affects plant growth. The data that you would collect would be quantitative – since you would measure the height of the plant in numbers.

Analyzing Data

After performing an experiment and collecting data, one must analyze the data. Research experiments are usually analyzed with statistical software in order to determine relationships among the data. In the case of a simpler experiment, one could simply look at the data and see how they correlate with the change in the independent variable.

Forming a Conclusion

The last step of the scientific method is to form a conclusion. If the data support the hypothesis, then the hypothesis may be the explanation for the phenomena. However, multiple trials must be done to confirm the results, and it is also important to make sure that the sample size—the number of observations made—is big enough so that the data is not skewed by just a few observations.

If the data do not support the hypothesis, then more observations must be made, a new hypothesis is formed, and the scientific method is used all over again. When a conclusion is drawn, the research can be presented to others to inform them of the findings and receive input about the validity of the conclusion drawn from the research.

Scientific Method Examples

There are very many examples of the use of the scientific method throughout history because it is the basis for all scientific experiments. Scientists have been conducting experiments using the scientific method for hundreds of years.

One such example is Francesco Redi’s experiment on spontaneous generation. In the 17 th Century, when Redi lived, people commonly believed that living things could spontaneously arise from organic material. For example, people believed that maggots were created from meat that was left out to sit. Redi had an alternate hypothesis: that maggots were actually part of the fly life cycle!

He conducted an experiment by leaving four jars of meat out: some uncovered, some covered with muslin, and some sealed completely. Flies got into the uncovered jars and maggots appeared a short time later. The jars that were covered had maggots on the outer surface of the muslin, but not inside the jars. Sealed jars had absolutely no maggots whatsoever.

Redi was able to conclude that maggots did not spontaneously arise in meat. He further confirmed the results by collecting captured maggots and growing them into adult flies. This may seem like common sense today, but back then, people did not know as much about the world, and it is through experiments like these that people uncovered what is now common knowledge.

Scientists use the scientific method in their research, but it is also used by people who aren’t scientists in everyday life. Even if you were not consciously aware of it, you have used the scientific method many times when solving problems around you.

For example, say you are at home and a lightbulb goes out. Noticing that the lightbulb is out is an observation. You would then naturally question, “Why is the lightbulb out?” and come up with possible guesses, or hypotheses. For example, you may hypothesize that the bulb has burned out. Then you would perform a very small experiment in order to test your hypothesis; namely, you would replace the bulb and analyze the data (“Did the light come back on?”).

If the light turned back on, you would conclude that the lightbulb had, in fact, burned out. But if the light still did not work, you would come up with other hypotheses (“The socket doesn’t work”, “Part of the lamp is broken,” “The fuse went out”, etc.) and test those.

1. Which step of the scientific method comes immediately after making observations and asking a question?

2. A scientist is performing an experiment to determine if the amount of light that rodents are exposed to affects their sleep cycle. She places some rodents in a room with 12 hours of light and 12 hours of darkness, some in a room with 24-hour light, and some in 24-hour darkness. What is the independent variable in this experiment?

3. What is the last step of the scientific method?

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1.2 The Process of Science

Learning objectives.

• Identify the shared characteristics of the natural sciences
• Understand the process of scientific inquiry
• Compare inductive reasoning with deductive reasoning
• Describe the goals of basic science and applied science

Like geology, physics, and chemistry, biology is a science that gathers knowledge about the natural world. Specifically, biology is the study of life. The discoveries of biology are made by a community of researchers who work individually and together using agreed-on methods. In this sense, biology, like all sciences is a social enterprise like politics or the arts. The methods of science include careful observation, record keeping, logical and mathematical reasoning, experimentation, and submitting conclusions to the scrutiny of others. Science also requires considerable imagination and creativity; a well-designed experiment is commonly described as elegant, or beautiful. Like politics, science has considerable practical implications and some science is dedicated to practical applications, such as the prevention of disease (see Figure 1.15 ). Other science proceeds largely motivated by curiosity. Whatever its goal, there is no doubt that science, including biology, has transformed human existence and will continue to do so.

The Nature of Science

Biology is a science, but what exactly is science? What does the study of biology share with other scientific disciplines? Science (from the Latin scientia, meaning "knowledge") can be defined as knowledge about the natural world.

Science is a very specific way of learning, or knowing, about the world. The history of the past 500 years demonstrates that science is a very powerful way of knowing about the world; it is largely responsible for the technological revolutions that have taken place during this time. There are however, areas of knowledge and human experience that the methods of science cannot be applied to. These include such things as answering purely moral questions, aesthetic questions, or what can be generally categorized as spiritual questions. Science cannot investigate these areas because they are outside the realm of material phenomena, the phenomena of matter and energy, and cannot be observed and measured.

The scientific method is a method of research with defined steps that include experiments and careful observation. The steps of the scientific method will be examined in detail later, but one of the most important aspects of this method is the testing of hypotheses. A hypothesis is a suggested explanation for an event, which can be tested. Hypotheses, or tentative explanations, are generally produced within the context of a scientific theory . A generally accepted scientific theory is thoroughly tested and confirmed explanation for a set of observations or phenomena. Scientific theory is the foundation of scientific knowledge. In addition, in many scientific disciplines (less so in biology) there are scientific laws , often expressed in mathematical formulas, which describe how elements of nature will behave under certain specific conditions. There is not an evolution of hypotheses through theories to laws as if they represented some increase in certainty about the world. Hypotheses are the day-to-day material that scientists work with and they are developed within the context of theories. Laws are concise descriptions of parts of the world that are amenable to formulaic or mathematical description.

Natural Sciences

What would you expect to see in a museum of natural sciences? Frogs? Plants? Dinosaur skeletons? Exhibits about how the brain functions? A planetarium? Gems and minerals? Or maybe all of the above? Science includes such diverse fields as astronomy, biology, computer sciences, geology, logic, physics, chemistry, and mathematics ( Figure 1.16 ). However, those fields of science related to the physical world and its phenomena and processes are considered natural sciences . Thus, a museum of natural sciences might contain any of the items listed above.

There is no complete agreement when it comes to defining what the natural sciences include. For some experts, the natural sciences are astronomy, biology, chemistry, earth science, and physics. Other scholars choose to divide natural sciences into life sciences , which study living things and include biology, and physical sciences , which study nonliving matter and include astronomy, physics, and chemistry. Some disciplines such as biophysics and biochemistry build on two sciences and are interdisciplinary.

Scientific Inquiry

One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. Two methods of logical thinking are used: inductive reasoning and deductive reasoning.

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative (descriptive) or quantitative (consisting of numbers), and the raw data can be supplemented with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and the analysis of a large amount of data. Brain studies often work this way. Many brains are observed while people are doing a task. The part of the brain that lights up, indicating activity, is then demonstrated to be the part controlling the response to that task.

Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid. For example, a prediction would be that if the climate is becoming warmer in a region, the distribution of plants and animals should change. Comparisons have been made between distributions in the past and the present, and the many changes that have been found are consistent with a warming climate. Finding the change in distribution is evidence that the climate change conclusion is a valid one.

Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science aims to observe, explore, and discover, while hypothesis-based science begins with a specific question or problem and a potential answer or solution that can be tested. The boundary between these two forms of study is often blurred, because most scientific endeavors combine both approaches. Observations lead to questions, questions lead to forming a hypothesis as a possible answer to those questions, and then the hypothesis is tested. Thus, descriptive science and hypothesis-based science are in continuous dialogue.

Hypothesis Testing

Biologists study the living world by posing questions about it and seeking science-based responses. This approach is common to other sciences as well and is often referred to as the scientific method. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) ( Figure 1.17 ), who set up inductive methods for scientific inquiry. The scientific method is not exclusively used by biologists but can be applied to almost anything as a logical problem-solving method.

The scientific process typically starts with an observation (often a problem to be solved) that leads to a question. Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. One Monday morning, a student arrives at class and quickly discovers that the classroom is too warm. That is an observation that also describes a problem: the classroom is too warm. The student then asks a question: “Why is the classroom so warm?”

Recall that a hypothesis is a suggested explanation that can be tested. To solve a problem, several hypotheses may be proposed. For example, one hypothesis might be, “The classroom is warm because no one turned on the air conditioning.” But there could be other responses to the question, and therefore other hypotheses may be proposed. A second hypothesis might be, “The classroom is warm because there is a power failure, and so the air conditioning doesn’t work.”

Once a hypothesis has been selected, a prediction may be made. A prediction is similar to a hypothesis but it typically has the format “If . . . then . . . .” For example, the prediction for the first hypothesis might be, “ If the student turns on the air conditioning, then the classroom will no longer be too warm.”

A hypothesis must be testable to ensure that it is valid. For example, a hypothesis that depends on what a bear thinks is not testable, because it can never be known what a bear thinks. It should also be falsifiable , meaning that it can be disproven by experimental results. An example of an unfalsifiable hypothesis is “Botticelli’s Birth of Venus is beautiful.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important. A hypothesis can be disproven, or eliminated, but it can never be proven. Science does not deal in proofs like mathematics. If an experiment fails to disprove a hypothesis, then we find support for that explanation, but this is not to say that down the road a better explanation will not be found, or a more carefully designed experiment will be found to falsify the hypothesis.

Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment. A control is a part of the experiment that does not change. Look for the variables and controls in the example that follows. As a simple example, an experiment might be conducted to test the hypothesis that phosphate limits the growth of algae in freshwater ponds. A series of artificial ponds are filled with water and half of them are treated by adding phosphate each week, while the other half are treated by adding a salt that is known not to be used by algae. The variable here is the phosphate (or lack of phosphate), the experimental or treatment cases are the ponds with added phosphate and the control ponds are those with something inert added, such as the salt. Just adding something is also a control against the possibility that adding extra matter to the pond has an effect. If the treated ponds show lesser growth of algae, then we have found support for our hypothesis. If they do not, then we reject our hypothesis. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted; it simply eliminates one hypothesis that is not valid ( Figure 1.18 ). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

In recent years a new approach of testing hypotheses has developed as a result of an exponential growth of data deposited in various databases. Using computer algorithms and statistical analyses of data in databases, a new field of so-called "data research" (also referred to as "in silico" research) provides new methods of data analyses and their interpretation. This will increase the demand for specialists in both biology and computer science, a promising career opportunity.

Visual Connection

In the example below, the scientific method is used to solve an everyday problem. Which part in the example below is the hypothesis? Which is the prediction? Based on the results of the experiment, is the hypothesis supported? If it is not supported, propose some alternative hypotheses.

• My toaster doesn’t toast my bread.
• Why doesn’t my toaster work?
• There is something wrong with the electrical outlet.
• If something is wrong with the outlet, my coffeemaker also won’t work when plugged into it.
• I plug my coffeemaker into the outlet.
• My coffeemaker works.

In practice, the scientific method is not as rigid and structured as it might at first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests.

Basic and Applied Science

The scientific community has been debating for the last few decades about the value of different types of science. Is it valuable to pursue science for the sake of simply gaining knowledge, or does scientific knowledge only have worth if we can apply it to solving a specific problem or bettering our lives? This question focuses on the differences between two types of science: basic science and applied science.

Basic science or “pure” science seeks to expand knowledge regardless of the short-term application of that knowledge. It is not focused on developing a product or a service of immediate public or commercial value. The immediate goal of basic science is knowledge for knowledge’s sake, though this does not mean that in the end it may not result in an application.

In contrast, applied science or “technology,” aims to use science to solve real-world problems, making it possible, for example, to improve a crop yield, find a cure for a particular disease, or save animals threatened by a natural disaster. In applied science, the problem is usually defined for the researcher.

Some individuals may perceive applied science as “useful” and basic science as “useless.” A question these people might pose to a scientist advocating knowledge acquisition would be, “What for?” A careful look at the history of science, however, reveals that basic knowledge has resulted in many remarkable applications of great value. Many scientists think that a basic understanding of science is necessary before an application is developed; therefore, applied science relies on the results generated through basic science. Other scientists think that it is time to move on from basic science and instead to find solutions to actual problems. Both approaches are valid. It is true that there are problems that demand immediate attention; however, few solutions would be found without the help of the knowledge generated through basic science.

One example of how basic and applied science can work together to solve practical problems occurred after the discovery of DNA structure led to an understanding of the molecular mechanisms governing DNA replication. Strands of DNA, unique in every human, are found in our cells, where they provide the instructions necessary for life. During DNA replication, new copies of DNA are made, shortly before a cell divides to form new cells. Understanding the mechanisms of DNA replication enabled scientists to develop laboratory techniques that are now used to identify genetic diseases, pinpoint individuals who were at a crime scene, and determine paternity. Without basic science, it is unlikely that applied science could exist.

Another example of the link between basic and applied research is the Human Genome Project, a study in which each human chromosome was analyzed and mapped to determine the precise sequence of DNA subunits and the exact location of each gene. (The gene is the basic unit of heredity represented by a specific DNA segment that codes for a functional molecule.) Other organisms have also been studied as part of this project to gain a better understanding of human chromosomes. The Human Genome Project ( Figure 1.19 ) relied on basic research carried out with non-human organisms and, later, with the human genome. An important end goal eventually became using the data for applied research seeking cures for genetically related diseases.

While research efforts in both basic science and applied science are usually carefully planned, it is important to note that some discoveries are made by serendipity, that is, by means of a fortunate accident or a lucky surprise. Penicillin was discovered when biologist Alexander Fleming accidentally left a petri dish of Staphylococcus bacteria open. An unwanted mold grew, killing the bacteria. The mold turned out to be Penicillium , and a new critically important antibiotic was discovered. In a similar manner, Percy Lavon Julian was an established medicinal chemist working on a way to mass produce compounds with which to manufacture important drugs. He was focused on using soybean oil in the production of progesterone (a hormone important in the menstrual cycle and pregnancy), but it wasn't until water accidentally leaked into a large soybean oil storage tank that he found his method. Immediately recognizing the resulting substance as stigmasterol, a primary ingredient in progesterone and similar drugs, he began the process of replicating and industrializing the process in a manner that has helped millions of people. Even in the highly organized world of science, luck—when combined with an observant, curious mind focused on the types of reasoning discussed above—can lead to unexpected breakthroughs.

Reporting Scientific Work

Whether scientific research is basic science or applied science, scientists must share their findings for other researchers to expand and build upon their discoveries. Communication and collaboration within and between sub disciplines of science are key to the advancement of knowledge in science. For this reason, an important aspect of a scientist’s work is disseminating results and communicating with peers. Scientists can share results by presenting them at a scientific meeting or conference, but this approach can reach only the limited few who are present. Instead, most scientists present their results in peer-reviewed articles that are published in scientific journals. Peer-reviewed articles are scientific papers that are reviewed, usually anonymously by a scientist’s colleagues, or peers. These colleagues are qualified individuals, often experts in the same research area, who judge whether or not the scientist’s work is suitable for publication. The process of peer review helps to ensure that the research described in a scientific paper or grant proposal is original, significant, logical, and thorough. Grant proposals, which are requests for research funding, are also subject to peer review. Scientists publish their work so other scientists can reproduce their experiments under similar or different conditions to expand on the findings.

There are many journals and the popular press that do not use a peer-review system. A large number of online open-access journals, journals with articles available without cost, are now available many of which use rigorous peer-review systems, but some of which do not. Results of any studies published in these forums without peer review are not reliable and should not form the basis for other scientific work. In one exception, journals may allow a researcher to cite a personal communication from another researcher about unpublished results with the cited author’s permission.

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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

Pingback: pligg.com

“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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The Scientific Method – Hypotheses, Models, Theories, and Laws

The scientific method is defined as the steps scientists follow to create a view of the world that is accurate, reliable, and consistent.  It’s also a way of minimizing how a scientist’s cultural and personal beliefs impact and influence their work.  It attempts to make a person’s perceptions and interpretations of nature and natural phenomena as scientific and neutral as possible.  It minimizes the amount of prejudice and bias a scientist has on the results of an experiment, hypothesis, or theory.

The scientific method can be broken down into four steps:

• Observe and describe the phenomenon (or group of various phenomena).
• Create a hypothesis that explains the phenomena. In physics, this often means creating a mathematical relation or a causal mechanism.
• Use this hypothesis to attempt to predict other related phenomena or the results of another set of observations.
• Test the performance of these predictions using independent experiments.

If the results of these experiments support the hypothesis, then it may become a theory or even a law of nature.  However, if they do not support the hypothesis, then it either has to be changed or completely rejected.  The main benefit of the scientific method is that it has predictive power—a proven theory can be applied to a wide range of phenomena.  Of course, even the most tested theory may be, at some point, proven wrong because new observations may be recorded or experiments done that contradict it.  Theories can never fully be proven, only fully disproven.

• The Steps of the Scientific Method – A basic introduction
• Wikipedia’s Entry for the Scientific Method – It goes into the history of the method
• Definition of the Scientific Method – Also includes a brief history of its use
• Steps of the Scientific Method – More detail about each of the steps

Testing Hypotheses

Testing a hypothesis can lead to one of two things: the hypothesis is confirmed or the hypothesis is rejected, meaning it either has to be changed or a new hypothesis has to be created.  This must happen if the experiments repeatedly and clearly show that their hypothesis is wrong.  It doesn’t matter how elegant or supported a theory is—if it can be disproven once, it can’t be considered a law of nature.  Experimentation is the supreme rule in the scientific method, and if an experiment shows that the hypothesis isn’t true, it trumps all previous experiments that supported it.  These experiments sometimes directly test the theory, while other times they test the theory indirectly via logic and math.  The scientific method requires that all theories have to be testable in some way—those that can’t are not considered scientific theories.

If a theory is disproven, that theory might still be applicable in some ways, but it’s no longer considered a true law of nature.  For example, Newton’s Laws were disproven in cases where the velocity is greater than the speed of light, but they can still be applied to mechanics that use slower velocities.  Other theories that were widely held to be true for years, even centuries, that have been disproven due to new observations include the idea that the earth is the center of our solar system or that the planets orbited the sun in perfect circular orbits rather than the now-proven elliptical orbits.

Of course, a hypothesis or proven theory isn’t always disproven by one single experiment.  This is because experiments may have errors in them, so a hypothesis that looks like it failed once is tested several times by several independent tests.  Things that can cause errors include faulty instruments, misreading measurements or other data, or the bias of the researcher.  Most measurements are given with a degree of error.  Scientists work to make that degree of error as small as possible while still estimating and calculating everything that could cause errors in a test.

• Testing Software Hypotheses – How to apply the scientific method to software testing
• Testing Scientific Ideas – Including a graph of the process
• Research Hypothesis Testing – What is it, and how is it tested?
• What Hypothesis Testing is All About – A different look at testing

Common Mistakes in Applying the Scientific Method

Unfortunately, the scientific method isn’t always applied correctly.  Mistakes do happen, and some of them are actually fairly common.  Because all scientists are human with biases and prejudices, it can be hard to be truly objective in some cases.  It’s important that all results are as untainted by bias as possible, but that doesn’t always happen. Another common mistake is taking something as common sense or deciding that something is so logical that it doesn’t need to be tested.  Scientists have to remember that everything has to be tested before it can be considered a solid hypothesis.

Scientists also have to be willing to look at every piece of data, even those which invalidate the hypothesis.  Some scientists so strongly believe their hypothesis that they try to explain away data that disproves it.  They want to find some reason as to why that data or experiment must be wrong instead of looking at their hypothesis again.  All data has to be considered in the same way, even if it goes against the hypothesis.

Another common issue is forgetting to estimate all possible errors that could arise during testing.  Some data that contradicts the hypothesis has been explained as falling into the range of error, but really, it was a systematic error that the researchers simply didn’t account for.

• Mistakes Young Researchers Make – 15 common errors new scientists may make
• Experimental Error – A look at false positives and false negatives
• Control of Measurement Errors – How to keep errors in measurement to a minimum
• Errors in Scientific Experiments – What they are and how to handle them

Hypotheses, Models, Theories, and Laws

While some people do incorrectly use words like “theory” and “hypotheses” interchangeably, the scientific community has very strict definitions of these terms.

Hypothesis:   A hypothesis is an observation, usually based on a cause and effect.  It is the basic idea that has not been tested.  A hypothesis is just an idea that explains something.  It must go through a number of experiments designed to prove or disprove it.

Model: A hypothesis becomes a model after some testing has been done and it appears to be a valid observation.  Some models are only valid in specific instances, such as when a value falls within a certain range.  A model may also be called a law.

Scientific theory: A model that has been repeatedly tested and confirmed may become a scientific theory.  These theories have been tested by a number of independent researchers around the world using various experiments, and all have supported the theory.  Theories may be disproven, of course, but only after rigorous testing of a new hypothesis that seems to contradict them.

• What is a Hypothesis? – The definition of a hypothesis and its function in the scientific method
• Hypothesis, Theory, and Law – Definitions of each
• 10 Scientific Laws and Theories – Some examples

The scientific method has been used for years to create hypotheses, test them, and develop them into full scientific theories.  While it appears to be a very simple method at first glance, it’s actually one of the most complex ways of testing and evaluating an observation or idea.  It’s different from other types of explanation because it attempts to remove all bias and move forward using systematic experimentation only.  However, like any method, there is room for error, such as bias or mechanical error.  Of course, just like the theories it tests, the scientific method may someday be revised.

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Psychological Research

The scientific process, learning objectives.

• Explain the steps of the scientific method
• Differentiate between theories and hypotheses

Figure 1 . Some of our ancestors, across the world and over the centuries, believed that trephination—the practice of making a hole in the skull, as shown here—allowed evil spirits to leave the body, thus curing mental illness and other disorders. (credit: “taiproject”/Flickr)

The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.

While behavior is observable, the mind is not. If someone is crying, we can see the behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This module explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.

Process of Scientific Research

Figure 2 . The scientific method is a process for gathering data and processing information. It provides well-defined steps to standardize how scientific knowledge is gathered through a logical, rational problem-solving method.

Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on.

The basic steps in the scientific method are:

• Observe a natural phenomenon and define a question about it
• Make a hypothesis, or potential solution to the question
• Test the hypothesis
• If the hypothesis is true, find more evidence or find counter-evidence
• If the hypothesis is false, create a new hypothesis or try again
• Draw conclusions and repeat–the scientific method is never-ending, and no result is ever considered perfect

In order to ask an important question that may improve our understanding of the world, a researcher must first observe natural phenomena. By making observations, a researcher can define a useful question. After finding a question to answer, the researcher can then make a prediction (a hypothesis) about what they think the answer will be. This prediction is usually a statement about the relationship between two or more variables. After making a hypothesis, the researcher will then design an experiment to test their hypothesis and evaluate the data gathered. These data will either support or refute the hypothesis. Based on the conclusions drawn from the data, the researcher will then find more evidence to support the hypothesis, look for counter-evidence to further strengthen the hypothesis, revise the hypothesis and create a new experiment, or continue to incorporate the information gathered to answer the research question.

Basic Principles of the Scientific Method

Two key concepts in the scientific approach are theory and hypothesis. A theory is a well-developed set of ideas that propose an explanation for observed phenomena that can be used to make predictions about future observations. A hypothesis is a testable prediction that is arrived at logically from a theory. It is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests.

Figure 3 . The scientific method involves deriving hypotheses from theories and then testing those hypotheses. If the results are consistent with the theory, then the theory is supported. If the results are not consistent, then the theory should be modified and new hypotheses will be generated.

Other key components in following the scientific method include verifiability, predictability, falsifiability, and fairness. Verifiability means that an experiment must be replicable by another researcher. To achieve verifiability, researchers must make sure to document their methods and clearly explain how their experiment is structured and why it produces certain results.

Predictability in a scientific theory implies that the theory should enable us to make predictions about future events. The precision of these predictions is a measure of the strength of the theory.

Falsifiability refers to whether a hypothesis can be disproved. For a hypothesis to be falsifiable, it must be logically possible to make an observation or do a physical experiment that would show that there is no support for the hypothesis. Even when a hypothesis cannot be shown to be false, that does not necessarily mean it is not valid. Future testing may disprove the hypothesis. This does not mean that a hypothesis has to be shown to be false, just that it can be tested.

To determine whether a hypothesis is supported or not supported, psychological researchers must conduct hypothesis testing using statistics. Hypothesis testing is a type of statistics that determines the probability of a hypothesis being true or false. If hypothesis testing reveals that results were “statistically significant,” this means that there was support for the hypothesis and that the researchers can be reasonably confident that their result was not due to random chance. If the results are not statistically significant, this means that the researchers’ hypothesis was not supported.

Fairness implies that all data must be considered when evaluating a hypothesis. A researcher cannot pick and choose what data to keep and what to discard or focus specifically on data that support or do not support a particular hypothesis. All data must be accounted for, even if they invalidate the hypothesis.

Applying the Scientific Method

To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later module, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

Remember that a good scientific hypothesis is falsifiable, or capable of being shown to be incorrect. Recall from the introductory module that Sigmund Freud had lots of interesting ideas to explain various human behaviors. However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

Figure 4 . Many of the specifics of (a) Freud’s theories, such as (b) his division of the mind into id, ego, and superego, have fallen out of favor in recent decades because they are not falsifiable. In broader strokes, his views set the stage for much of psychological thinking today, such as the unconscious nature of the majority of psychological processes.

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Link to Learning

Want to participate in a study? Visit this Psychological Research on the Net website and click on a link that sounds interesting to you in order to participate in online research.

Why the Scientific Method Is Important for Psychology

The use of the scientific method is one of the main features that separates modern psychology from earlier philosophical inquiries about the mind. Compared to chemistry, physics, and other “natural sciences,” psychology has long been considered one of the “social sciences” because of the subjective nature of the things it seeks to study. Many of the concepts that psychologists are interested in—such as aspects of the human mind, behavior, and emotions—are subjective and cannot be directly measured. Psychologists often rely instead on behavioral observations and self-reported data, which are considered by some to be illegitimate or lacking in methodological rigor. Applying the scientific method to psychology, therefore, helps to standardize the approach to understanding its very different types of information.

The scientific method allows psychological data to be replicated and confirmed in many instances, under different circumstances, and by a variety of researchers. Through replication of experiments, new generations of psychologists can reduce errors and broaden the applicability of theories. It also allows theories to be tested and validated instead of simply being conjectures that could never be verified or falsified. All of this allows psychologists to gain a stronger understanding of how the human mind works.

Scientific articles published in journals and psychology papers written in the style of the American Psychological Association (i.e., in “APA style”) are structured around the scientific method. These papers include an introduction, which introduces the background information and outlines the hypotheses; a methods section, which outlines the specifics of how the experiment was conducted to test the hypothesis; a results section, which includes the statistics that tested the hypothesis and state whether it was supported or not supported, and a discussion and conclusion, which state the implications of finding support for, or no support for, the hypothesis. Writing articles and papers that adhere to the scientific method makes it easy for future researchers to repeat the study and attempt to replicate the results.

Notable Researchers

Psychological research has a long history involving important figures from diverse backgrounds. While the introductory module discussed several researchers who made significant contributions to the discipline, there are many more individuals who deserve attention in considering how psychology has advanced as a science through their work. For instance, Margaret Floy Washburn (1871–1939) was the first woman to earn a PhD in psychology. Her research focused on animal behavior and cognition (Margaret Floy Washburn, PhD, n.d.). Mary Whiton Calkins (1863–1930) was a preeminent first-generation American psychologist who opposed the behaviorist movement, conducted significant research into memory, and established one of the earliest experimental psychology labs in the United States (Mary Whiton Calkins, n.d.).

Figure 5 . (a) Margaret Floy Washburn was the first woman to earn a doctorate degree in psychology. (b) Psychologist Inez Beverly Prosser, who was the first African American woman to earn a PhD in psychology.

Francis Sumner (1895–1954) was the first African American to receive a PhD in psychology in 1920. His dissertation focused on issues related to psychoanalysis. Sumner also had research interests in racial bias and educational justice. Sumner was one of the founders of Howard University’s department of psychology, and because of his accomplishments, he is sometimes referred to as the “Father of Black Psychology.” Thirteen years later, Inez Beverly Prosser (1895–1934) became the first African American woman to receive a PhD in psychology. Prosser’s research highlighted issues related to education in segregated versus integrated schools, and ultimately, her work was very influential in the hallmark Brown v. Board of Education Supreme Court ruling that segregation of public schools was unconstitutional (Ethnicity and Health in America Series: Featured Psychologists, n.d.).

Although the establishment of psychology’s scientific roots occurred first in Europe and the United States, it did not take much time until researchers from around the world began to establish their own laboratories and research programs. For example, some of the first experimental psychology laboratories in South America were founded by Horatio Piñero (1869–1919) at two institutions in Buenos Aires, Argentina (Godoy & Brussino, 2010). In India, Gunamudian David Boaz (1908–1965) and Narendra Nath Sen Gupta (1889–1944) established the first independent departments of psychology at the University of Madras and the University of Calcutta, respectively. These developments provided an opportunity for Indian researchers to make important contributions to the field (Gunamudian David Boaz, n.d.; Narendra Nath Sen Gupta, n.d.).

When the American Psychological Association (APA) was first founded in 1892, all of the members were white males. However, by 1905, Mary Whiton Calkins was elected as the first female president of the APA, and by 1946, nearly one-quarter of American psychologists were female. Psychology became a popular degree option for students enrolled in the nation’s historically black higher education institutions, increasing the number of black Americans who went on to become psychologists. Given demographic shifts occurring in the United States and increased access to higher educational opportunities among historically underrepresented populations, there is reason to hope that the diversity of the field will increasingly match the larger population, and that the research contributions made by the psychologists of the future will better serve people of all backgrounds (Women and Minorities in Psychology, n.d.).

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What are the three things all experiments must be?

Experts agree that in order for an experiment to be counted as 'good science' or a 'good experiment', they must contain three things:

• Data collected from the experiment, usually put into a table/chart/graph
• A conclusion drawn from the experiment

~Information obtained from: http://www.chacha.com/question/what-are-least-three-things-you-should-be-careful-to-do-when-designing-an-experiment

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Add your answer:

Why is it important for experiments to be written in scientific method?

All experiments are based on hypothesis that has to be tested for truth. All scientific experiments therefore follow a logical methodology to arrive at a conclusion that must have a universal result that becomes universal accepted truth in Scientific experiments. It is necessary to follow the universal methodology by collection of the data for analysis to determine the elements or functional relationship in the experimental process. It is similar to any mathematical function that proceeds from one step to the next with the application of a universal formula that is written when solved.

Do all experiments need to have a control group?

yes all experiments need to have a control

Do all experiments have a control?

All properly-designed experiments should have some sort of control.

What do all good experiments have in common?

A constant.

In order to be considered scientific a hypothesis must be?

It is necessary for a hypothesis to have two things, the words IF and THEN. Another word can be added, BECAUSE. A successful hypothesis has to have all three.

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• Open access
• Published: 24 July 2024

AI models collapse when trained on recursively generated data

• Ilia Shumailov 1   na1 ,
• Zakhar Shumaylov 2   na1 ,
• Yiren Zhao   ORCID: orcid.org/0000-0002-3727-7463 3 ,
• Nicolas Papernot 4 , 5 ,
• Ross Anderson   ORCID: orcid.org/0000-0001-8697-5682 6 , 7   na2 &
• Yarin Gal   ORCID: orcid.org/0000-0002-2733-2078 1  

Nature volume  631 ,  pages 755–759 ( 2024 ) Cite this article

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• Computer science

Stable diffusion revolutionized image creation from descriptive text. GPT-2 (ref.  1 ), GPT-3(.5) (ref.  2 ) and GPT-4 (ref.  3 ) demonstrated high performance across a variety of language tasks. ChatGPT introduced such language models to the public. It is now clear that generative artificial intelligence (AI) such as large language models (LLMs) is here to stay and will substantially change the ecosystem of online text and images. Here we consider what may happen to GPT-{ n } once LLMs contribute much of the text found online. We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear. We refer to this effect as ‘model collapse’ and show that it can occur in LLMs as well as in variational autoencoders (VAEs) and Gaussian mixture models (GMMs). We build theoretical intuition behind the phenomenon and portray its ubiquity among all learned generative models. We demonstrate that it must be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of LLM-generated content in data crawled from the Internet.

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Bias of AI-generated content: an examination of news produced by large language models

Augmenting interpretable models with large language models during training

The neural coding framework for learning generative models

The development of LLMs is very involved and requires large quantities of training data. Yet, although current LLMs 2 , 4 , 5 , 6 , including GPT-3, were trained on predominantly human-generated text, this may change. If the training data of most future models are also scraped from the web, then they will inevitably train on data produced by their predecessors. In this paper, we investigate what happens when text produced by, for example, a version of GPT forms most of the training dataset of following models. What happens to GPT generations GPT-{ n } as n increases? We discover that indiscriminately learning from data produced by other models causes ‘model collapse’—a degenerative process whereby, over time, models forget the true underlying data distribution, even in the absence of a shift in the distribution over time. We give examples of model collapse for GMMs, VAEs and LLMs. We show that, over time, models start losing information about the true distribution, which first starts with tails disappearing, and learned behaviours converge over the generations to a point estimate with very small variance. Furthermore, we show that this process is inevitable, even for cases with almost ideal conditions for long-term learning, that is, no function estimation error. We also briefly mention two close concepts to model collapse from the existing literature: catastrophic forgetting arising in the framework of task-free continual learning 7 and data poisoning 8 , 9 maliciously leading to unintended behaviour. Neither is able to explain the phenomenon of model collapse fully, as the setting is fundamentally different, but they provide another perspective on the observed phenomenon and are discussed in more depth in the  Supplementary Materials . Finally, we discuss the broader implications of model collapse. We note that access to the original data distribution is crucial: in learning tasks in which the tails of the underlying distribution matter, one needs access to real human-produced data. In other words, the use of LLMs at scale to publish content on the Internet will pollute the collection of data to train their successors: data about human interactions with LLMs will be increasingly valuable.

What is model collapse?

Definition 2.1 (model collapse).

Model collapse is a degenerative process affecting generations of learned generative models, in which the data they generate end up polluting the training set of the next generation. Being trained on polluted data, they then mis-perceive reality. The process is depicted in Fig. 1a . We separate two special cases: early model collapse and late model collapse. In early model collapse, the model begins losing information about the tails of the distribution; in late model collapse, the model converges to a distribution that carries little resemblance to the original one, often with substantially reduced variance.

This process occurs owing to three specific sources of error compounding over generations and causing deviation from the original model:

Statistical approximation error. This is the primary type of error, which arises owing to the number of samples being finite, and disappears as the number of samples tends to infinity. This occurs because of a non-zero probability that information can get lost at every step of resampling.

Functional expressivity error. This is a secondary type of error, arising owing to limited function approximator expressiveness. In particular, neural networks are only universal approximators as their size goes to infinity. As a result, a neural network can introduce non-zero likelihood outside the support of the original distribution or zero likelihood inside the support of the original distribution. A simple example of the expressivity error is if we tried fitting a mixture of two Gaussians with a single Gaussian. Even if we have perfect information about the data distribution (that is, infinite number of samples), model errors will be inevitable. However, in the absence of the other two types of error, this can only occur at the first generation.

Functional approximation error. This is a secondary type of error, arising primarily from the limitations of learning procedures, for example, structural bias of stochastic gradient descent 10 , 11 or choice of objective 12 . This error can be viewed as one arising in the limit of infinite data and perfect expressivity at each generation.

Each of the above can cause model collapse to get worse or better. More approximation power can even be a double-edged sword—better expressiveness may counteract statistical noise, resulting in a good approximation of the true distribution, but it can equally compound the noise. More often than not, we get a cascading effect, in which individual inaccuracies combine to cause the overall error to grow. For example, overfitting the density model causes the model to extrapolate incorrectly and assigns high-density regions to low-density regions not covered in the training set support; these will then be sampled with arbitrary frequency. It is worth noting that other types of error exist. For example, computers have limited precision in practice. We now turn to mathematical intuition to explain how the above give rise to the errors observed, how different sources can compound and how we can quantify the average model divergence.

Theoretical intuition

Here we provide a theoretical intuition for the phenomenon of model collapse. We argue that the process of model collapse is universal among generative models that recursively train on data generated by previous generations. We quantify the sources of errors discussed in the previous section by examining two mathematical models, which prove to be simple enough to provide analytical expressions for quantities of interest, but also portray the phenomenon of model collapse: a discrete distribution in the absence of functional expressivity and approximation errors, and a multidimensional Gaussian approximation, portraying joint functional expressivity and statistical errors. We further illustrate the impact of all three jointly for a more complex setting of density estimation in Hilbert spaces in the Supplementary Materials .

The overall stochastic process we consider, which we call learning with generational data, is the following. The dataset at generation i is \({{\mathcal{D}}}_{i}\) , comprising independent and identically distributed random variables \({X}_{j}^{i}\) with distribution p i , j   ∈  {1,…,  M i } denotes the size of the dataset. Going from generation i to generation i  + 1, we aim to estimate the distribution of samples in \({{\mathcal{D}}}_{i}\) , with an approximation \({p}_{{\theta }_{i+1}}\) . This step is what we refer to as functional approximation, \({p}_{{\theta }_{i+1}}={{\mathcal{F}}}_{\theta }({p}_{i})\) . The dataset \({{\mathcal{D}}}_{i+1}\) is then generated by sampling from \({p}_{i+1}={\alpha }_{i}{p}_{{\theta }_{i+1}}+{\beta }_{i}{p}_{i}+{\gamma }_{i}{p}_{0}\) , with non-negative parameters α i ,  β i ,  γ i summing to 1, that is, they represent proportions of data used from different generations. This corresponds to a mixing of data coming from the original distribution ( γ i ), data used by the previous generation ( β i ) and data generated by the new model ( α i ). We refer to this as the sampling step. For the mathematical models to come, we consider α i  =  γ i  = 0, that is, data only from a single step are used, whereas numerical experiments are performed on more realistic choices of parameters.

Discrete distributions with exact approximation

In this subsection, we consider a discrete probability distribution in absence of functional approximation and expressivity errors, that is, \({\mathcal{F}}(p)=p\) . In this case, model collapse arises only because of statistical errors from the sampling step. At first, the tails (low-probability events) begin to disappear as a result of the low probability of sampling them and, over time, support of the distribution shrinks. Denoting the sample size as M , if we consider state i with probability \(q\le \frac{1}{M}\) , the expected number of samples with value i coming from those events will be less than 1. In practice, this would mean that we lose information about them. Considering more generally some state i with probability q , using standard conditional probability, we can show that the probability of losing information (that is, sampling no data at some generation) is equal to 1 −  q , implying that the distribution must converge to a delta function positioned at some state, with the probability of ending up at a certain state equal to the probability of sampling said state from the original distribution.

This can be shown directly by considering the process \({{\bf{X}}}^{i}\to {\mathcal{F}}\,\to \) \({p}_{i+1}\to {{\bf{X}}}^{i+1}\) as a Markov chain, as X i +1 only depends on X i . Furthermore, if all the \({X}_{j}^{i}\) have the same value, then at the next generation, the approximated distribution will be exactly a delta function and therefore all of \({X}_{j}^{i+1}\) will also have the same value. This implies that the Markov chain contains at least one absorbing state and therefore, with probability 1, it will converge to one of the absorbing states. This is a well-known fact, of which a proof is provided in the Supplementary Materials . For this chain, the only absorbing states are those corresponding to delta functions. As a result, as we follow the progress of model collapse, we are guaranteed to end up in a constant state, having lost all the information of the original distribution when the chain is absorbed. This argument also works in general owing to floating-point representations being discrete, making the Markov chain over the parameters of the model discrete. Thus, as long as the model parameterization allows for delta functions, we will get to it, because—owing to sampling errors—the only possible absorbing states are delta functions. On the basis of the discussion above, we see how both early model collapse, in which only the low-probability events get cut off, and late stage model collapse, in which the process begins to collapse into a single mode, must arise in the case of discrete distributions with perfect functional approximation.

Multidimensional Gaussian

Following the discussion about discrete distributions, we now present a more generic result, which can be shown in the Gaussian approximation setting, in which each generation is approximated using the unbiased estimates of the mean and the variance. A similar result holds more generally, which we detail in the  Supplementary Materials .

Theorem 3.1 (Gaussian model collapse)

Assume the original data are sampled from distribution \({{\mathcal{D}}}_{0}\) (not necessarily Gaussian), with non-zero sample variance. Assume X n are fit recursively using the unbiased sample mean and variance estimators from the previous generation, \({X}_{j}^{n}| {\mu }_{n},{\Sigma }_{n} \sim {\mathcal{N}}({\mu }_{n},{\Sigma }_{n})\) , with a fixed sample size. Then,

in which \({{\mathbb{W}}}_{2}\) denotes the Wasserstein-2 distance between the true distribution and its approximation at generation n .

In words, this implies that not only does the n th generation approximation diverge arbitrarily far from the original one but it also collapses to be zero variance as the number of generations increases, with probability 1. The results are very analogous to that seen in the discrete case, with this theorem illustrating the effect of late stage model collapse, in which the process begins to collapse to be zero variance. The early stage model collapse can also be seen and the interested reader is referred to the  Supplementary Materials for a more in-depth discussion.

Model collapse in language models

In this section, we evaluate the effect of model collapse on language models. We cover more interpretable machine learning models—VAEs and GMMs—in the  Supplementary Materials . Code is publically available in ref.  13 .

Model collapse is universal across various families of machine learning models. Yet, if small models such as GMMs and VAEs are normally trained from scratch, LLMs are different. They are so expensive to retrain from scratch that they are typically initialized with pre-trained models such as BERT 4 , RoBERTa 5 or GPT-2 (ref.  2 ), which are trained on large text corpora. They are then fine-tuned to various downstream tasks 14 .

Here we explore what happens with language models when they are sequentially fine-tuned with data generated by other models. We can easily replicate all experiments covered in this paper with larger language models in non-fine-tuning settings to demonstrate model collapse. Given that training a single moderately large model produces twice the American lifetime’s worth of CO 2 (ref.  15 ), we opted to not run such an experiment and instead focus on a more realistic setting for a proof of concept. Note that even the language experiments described in this paper took weeks to run. We evaluate the most common setting of training a language model—a fine-tuning setting for which each of the training cycles starts from a pre-trained model with recent data. The data here come from another fine-tuned pre-trained model. Because training is restricted to produce models that are close to the original pre-trained model, and data points generated by the models will generally produce very small gradients, the expectation here may be that the model should only change moderately after fine-tuning. We fine-tune the OPT-125m causal language model made available by Meta through Hugging Face 6 .

We fine-tune it on the wikitext2 dataset 16 . For data generation from the trained models, we use a five-way beam search. We block training sequences to be 64 tokens long; then, for each token sequence in the training set, we ask the model to predict the next 64 tokens. We go through all of the original training dataset and produce an artificial dataset of the same size. Because we go through all of the original dataset and predict all of the blocks, if the model had 0 error, it would produce the original wikitext2 dataset. Training for each generation starts with generation from the original training data. Each experiment is run five times and the results are shown as five separate runs with different randomness seeds. The original model fine-tuned with real wikitext2 data obtains 34 mean perplexity, from the zero-shot baseline of 115, that is, it successfully learns the task. Finally, to be as realistic as possible, we use the best-performing model on the original task, evaluated using the original wikitext2 validation set, as the base model for the subsequent generations, meaning that—in practice—observed model collapse can be even more pronounced. Here we consider two different settings:

Five epochs, no original training data. Here the model is trained for five epochs starting on the original dataset but with no original data retained for subsequent runs. The overall original task performance is presented in Fig. 1b . We find that training with generated data allows us to adapt to the underlying task, losing some performance, from 20 to 28 perplexity points.

Ten epochs, 10% of original training data preserved. Here the model is trained for ten epochs on the original dataset and with every new generation of training, a random 10% of the original data points is sampled. The overall original task performance is presented in Fig. 1c . We find that preservation of the original data allows for better model fine-tuning and leads to only minor degradation of performance.

Both training regimes lead to degraded performance in our models, yet we do find that learning with generated data is possible and models can successfully learn (some of) the underlying task. In particular, from Fig. 1 and their 3D versions in the  Supplementary Materials , we see that model collapse occurs, as the density of samples with low perplexity begins to accumulate over the generations. This in turn makes it likely that, over the generations, the sampled data will similarly collapse to a delta function.

a , Model collapse refers to a degenerative learning process in which models start forgetting improbable events over time, as the model becomes poisoned with its own projection of reality. Here data are assumed to be human-curated and start off clean; then model 0 is trained and data are sampled from it; at step n , data are added to the overall data from step n  − 1 and this combination is used to train model n . Data obtained with Monte Carlo sampling should ideally be statistically close to the original, provided that fitting and sampling procedures are perfect. This process depicts what happens in real life with the Internet: model-generated data become pervasive. b , c , Performance of OPT-125m models of different generations evaluated using the original wikitext2 test dataset. Shown on the left are the histograms of perplexities of each individual data training sequence produced by different generations as evaluated by the very first model trained with the real data. Over the generations, models tend to produce samples that the original model trained with real data is more likely to produce. At the same time, a much longer tail appears for later generations. Later generations start producing samples that would never be produced by the original model, that is, they start misperceiving reality based on errors introduced by their ancestors. The same plots are shown in 3D in the Supplementary Materials . On the right, average perplexity and its standard deviation are shown for each independent run. The x axis refers to the generation of the model. ‘Real’ refers to the ‘model 0’ trained on the original wikitext2 dataset; model 1 was trained on the data produced by model 0, model 2 was trained on data produced by model 1 and so on, with all generated datasets equal in size. We find that models trained on generated data are able to learn some of the original task, but with errors, as seen from the increase in perplexity.

It is important to note here that the observed behaviour is in line with the general intuition established in the section ‘Theoretical intuition’. To be precise, in all experiments, generational learning is only performed on a finite (usually small) number of generations, whereas claims of the section ‘Theoretical intuition’ are mostly presented in the limit of generations going to infinity. However, as seen from experiments on VAEs and GMMs in the  Supplementary Materials , convergence to delta functions and specific rates of such convergence are highly related to the specifics of the problem considered, and complete collapse may or may not occur, even after a small number of steps. This is further illustrated theoretically in the  Supplementary Materials , in which potentially notable divergence from the original model can occur even after a few generations.

Figure 1b,c on the left shows histograms of individual data-point perplexities generated by the models of different generations as is evaluated by the first model developed with real wikitext2 training data. Here, over the generations, models tend to produce more sequences that the original model would produce with the higher likelihood. The observed effect is similar to that described for VAEs and GMMs in the  Supplementary Materials , in which—over the generations—models started to produce samples that would be produced with higher probabilities by the original model. At the same time, we discover that generated data have much longer tails, suggesting that some of the data would never be produced by the original model—these are the errors that accumulate because of the learning with generational data.

Example of text outputs of an OPT-125m model affected by model collapse—models degrade over generations, for which each new generation is trained on data produced by the previous generation.

Input: some started before 1360 — was typically accomplished by a master mason and a small team of itinerant masons, supplemented by local parish labourers, according to Poyntz Wright. But other authors reject this model, suggesting instead that leading architects designed the parish church towers based on early examples of Perpendicular.

Gen 0: Revival architecture such as St. John’s Cathedral in London. The earliest surviving example of Perpendicular Revival architecture is found in the 18th @-@ century Church of Our Lady of Guernsey, which dates from the late 19th century. There are two types of perpendicular churches : those.

Gen 1: architecture such as St. Peter’s Basilica in Rome or St. Peter’s Basilica in Buenos Aires. There is no evidence that any of these buildings were built during the reign of Pope Innocent III, but it is possible that they may have been built during the reign of his successor, Pope Innocent.

Gen 5: ism, which had been translated into more than 100 languages including English, French, German, Italian, Spanish, Portuguese, Dutch, Swedish, Norwegian, Polish, Hungarian, Slovak, Lithuanian, Estonian, Finnish, Romanian, Bulgarian, Turkish, Croatian, Serbian, Ukrainian, Russian, Kazakh, Kyrgyz.

Gen 9: architecture. In addition to being home to some of the world’s largest populations of black @-@ tailed jackrabbits, white @-@ tailed jackrabbits, blue @-@ tailed jackrabbits, red @-@ tailed jackrabbits, yellow @-.

Ablation: Repetitions

We find that data generated by language models in our experiments end up containing a large number of repeating phrases, as in Example 1. The repeating problem has been observed in nearly all text-generation models 17 , 18 and, to rule this out as the cause of model collapse, we further provide numerical experiments when models are explicitly encouraged to produce non-repeating sequences with a repeating penalty of 2.0. We find that this causes the models to produce lower score continuations to avoid using repeats, which—as a result—causes the consequent models to perform even worse. Model perplexities shift across the generations towards more probable token sequences, as measured using the model trained on the original real data distribution. Further illustrations are provided in the  Supplementary Materials . In particular, enforcing this for the LLM experiments causes the perplexity to double compared with the original. Models remain as susceptible to model collapse, if not more.

The described process demonstrates that fine-tuning of language models does not curb the effects of model collapse and models that are being fine-tuned are also vulnerable. We find that, over the generations, models tend to produce more probable sequences from the original data and start introducing their own improbable sequences, that is, errors.

We now discuss the implications of model collapse on the underlying learning dynamics of LLMs. Long-term poisoning attacks on language models are not new. For example, we saw the creation of click, content and troll farms, a form of human ‘language models’, whose job is to misguide social networks and search algorithms. The negative effect that these poisoning attacks had on search results led to changes in search algorithms. For example, Google downgraded farmed articles 19 , putting more emphasis on content produced by trustworthy sources, such as education domains, whereas DuckDuckGo removed them altogether 20 . What is different with the arrival of LLMs is the scale at which such poisoning can happen once it is automated. Preserving the ability of LLMs to model low-probability events is essential to the fairness of their predictions: such events are often relevant to marginalized groups. Low-probability events are also vital to understand complex systems 21 .

Our evaluation suggests a ‘first mover advantage’ when it comes to training models such as LLMs. In our work, we demonstrate that training on samples from another generative model can induce a distribution shift, which—over time—causes model collapse. This in turn causes the model to mis-perceive the underlying learning task. To sustain learning over a long period of time, we need to make sure that access to the original data source is preserved and that further data not generated by LLMs remain available over time. The need to distinguish data generated by LLMs from other data raises questions about the provenance of content that is crawled from the Internet: it is unclear how content generated by LLMs can be tracked at scale. One option is community-wide coordination to ensure that different parties involved in LLM creation and deployment share the information needed to resolve questions of provenance. Otherwise, it may become increasingly difficult to train newer versions of LLMs without access to data that were crawled from the Internet before the mass adoption of the technology or direct access to data generated by humans at scale.

Data availability

Data generation code for GMM experiments is available in ref.  13 . Data used for VAE experiments are available in ref.  22 . Data used for LLM experiments are available in ref.  16 .

Code availability

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Acknowledgements

This paper is dedicated to the memory of Professor Ross J. Anderson, our colleague and friend, who contributed much to this and other works we have produced over the years. We thank A. Thudi, D. Glukhov, P. Zaika, and D. Barak for useful discussions and feedback.

Author information

These authors contributed equally: Ilia Shumailov, Zakhar Shumaylov

Deceased: Ross Anderson

Authors and Affiliations

OATML, Department of Computer Science, University of Oxford, Oxford, UK

Ilia Shumailov & Yarin Gal

Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK

Zakhar Shumaylov

Department of Electrical and Electronic Engineering, Imperial College London, London, UK

University of Toronto, Toronto, Ontario, Canada

Nicolas Papernot

Vector Institute, Toronto, Ontario, Canada

Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

Ross Anderson

School of Informatics, University of Edinburgh, Edinburgh, UK

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Contributions

I.S. and Z.S. proposed and developed the idea, led the research and mathematical modelling and developed the GMM and VAE experiments. I.S. and Y.Z. developed the language-model experiments. N.P., Y.G. and R.A. supervised and guided the project. All authors contributed to writing of the manuscript. Y.G. is supported by a Turing AI Fellowship financed by the UK government’s Office for Artificial Intelligence, through UK Research and Innovation (grant reference EP/V030302/1) and delivered by the Alan Turing Institute.

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Correspondence to Ilia Shumailov , Zakhar Shumaylov or Yarin Gal .

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Shumailov, I., Shumaylov, Z., Zhao, Y. et al. AI models collapse when trained on recursively generated data. Nature 631 , 755–759 (2024). https://doi.org/10.1038/s41586-024-07566-y

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