what is the scientific law hypothesis

Scientific Hypothesis, Model, Theory, and Law

Understanding the Difference Between Basic Scientific Terms

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• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

Words have precise meanings in science. For example, "theory," "law," and "hypothesis" don't all mean the same thing. Outside of science, you might say something is "just a theory," meaning it's a supposition that may or may not be true. In science, however, a theory is an explanation that generally is accepted to be true. Here's a closer look at these important, commonly misused terms.

A hypothesis is an educated guess, based on observation. It's a prediction of cause and effect. Usually, a hypothesis can be supported or refuted through experimentation or more observation. A hypothesis can be disproven but not proven to be true.

Example: If you see no difference in the cleaning ability of various laundry detergents, you might hypothesize that cleaning effectiveness is not affected by which detergent you use. This hypothesis can be disproven if you observe a stain is removed by one detergent and not another. On the other hand, you cannot prove the hypothesis. Even if you never see a difference in the cleanliness of your clothes after trying 1,000 detergents, there might be one more you haven't tried that could be different.

Scientists often construct models to help explain complex concepts. These can be physical models like a model volcano or atom  or conceptual models like predictive weather algorithms. A model doesn't contain all the details of the real deal, but it should include observations known to be valid.

Example: The  Bohr model shows electrons orbiting the atomic nucleus, much the same way as the way planets revolve around the sun. In reality, the movement of electrons is complicated but the model makes it clear that protons and neutrons form a nucleus and electrons tend to move around outside the nucleus.

A scientific theory summarizes a hypothesis or group of hypotheses that have been supported with repeated testing. A theory is valid as long as there is no evidence to dispute it. Therefore, theories can be disproven. Basically, if evidence accumulates to support a hypothesis, then the hypothesis can become accepted as a good explanation of a phenomenon. One definition of a theory is to say that it's an accepted hypothesis.

Example: It is known that on June 30, 1908, in Tunguska, Siberia, there was an explosion equivalent to the detonation of about 15 million tons of TNT. Many hypotheses have been proposed for what caused the explosion. It was theorized that the explosion was caused by a natural extraterrestrial phenomenon , and was not caused by man. Is this theory a fact? No. The event is a recorded fact. Is this theory, generally accepted to be true, based on evidence to-date? Yes. Can this theory be shown to be false and be discarded? Yes.

A scientific law generalizes a body of observations. At the time it's made, no exceptions have been found to a law. Scientific laws explain things but they do not describe them. One way to tell a law and a theory apart is to ask if the description gives you the means to explain "why." The word "law" is used less and less in science, as many laws are only true under limited circumstances.

Example: Consider Newton's Law of Gravity . Newton could use this law to predict the behavior of a dropped object but he couldn't explain why it happened.

As you can see, there is no "proof" or absolute "truth" in science. The closest we get are facts, which are indisputable observations. Note, however, if you define proof as arriving at a logical conclusion, based on the evidence, then there is "proof" in science. Some work under the definition that to prove something implies it can never be wrong, which is different. If you're asked to define the terms hypothesis, theory, and law, keep in mind the definitions of proof and of these words can vary slightly depending on the scientific discipline. What's important is to realize they don't all mean the same thing and cannot be used interchangeably.

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1.3: Hypothesis, Theories, and Laws

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Learning Objectives

• Describe the difference between hypothesis, theory as scientific terms.
• Describe the difference between a theory and scientific law.

Although all of us have taken science classes throughout the course of our study, many people have incorrect or misleading ideas about some of the most important and basic principles in science. We have all heard of hypotheses, theories, and laws, but what do they really mean? Before you read this section, think about what you have learned about these terms before. What do these terms mean to you? What do you read contradicts what you thought? What do you read supports what you thought?

What is a Fact?

A fact is a basic statement establish by experiment or observation. All facts are true under the specific conditions of the observation.

What is a Hypothesis?

One of the most common terms used in science classes is a "hypothesis". The word can have many different definitions, depending on the context in which it is being used:

• "An educated guess" - because it provides a suggested solution based on evidence to be a scientific hypothesis
• Prediction - if you have ever carried out a science experiment, you probably made this type of hypothesis, in which you predicted the outcome of your experiment.
• Tentative or Proposed explanation - hypotheses can be suggestions about why something is observed, but in order for it to be scientific, we must be able to test the explanation to see if it works, if it is able to correctly predict what will happen in a situation, such as: if my hypothesis is correct, we should see ___ result when we perform ___ test.

A hypothesis is very tentative; it can be easily changed.

What is a Theory?

The United States National Academy of Sciences describes what a theory is as follows:

"Some scientific explanations are so well established that no new evidence is likely to alter them. The explanation becomes a scientific theory. In everyday language a theory means a hunch or speculation. Not so in science. In science, the word theory refers to a comprehensive explanation of an important feature of nature supported by facts gathered over time. Theories also allow scientists to make predictions about as yet unobserved phenomena."

"A scientific theory is a well-substantiated explanation of some aspect of the natural world, based on a body of facts that have been repeatedly confirmed through observation and experimentation. Such fact-supported theories are not "guesses" but reliable accounts of the real world. The theory of biological evolution is more than "just a theory." It is as factual an explanation of the universe as the atomic theory of matter (stating that everything is made of atoms) or the germ theory of disease (which states that many diseases are caused by germs). Our understanding of gravity is still a work in progress. But the phenomenon of gravity, like evolution, is an accepted fact."

Not some key features of theories that are important to understand from this description:

• Theories are explanations of natural phenomenon. They aren't predictions (although we may use theories to make predictions). They are explanations why we observe something.
• Theories aren't likely to change. They have so much support and are able to explain satisfactorily so many observations, that they are not likely to change. Theories can, indeed, be facts. Theories can change, but it is a long and difficult process. In order for a theory to change, there must be many observations or evidence that the theory cannot explain.
• Theories are not guesses. The phrase "just a theory" has no room in science. To be a scientific theory carries a lot of weight; it is not just one person's idea about something

Theories aren't likely to change.

What is a Law?

Scientific laws are similar to scientific theories in that they are principles that can be used to predict the behavior of the natural world. Both scientific laws and scientific theories are typically well-supported by observations and/or experimental evidence. Usually scientific laws refer to rules for how nature will behave under certain conditions, frequently written as an equation. Scientific theories are more overarching explanations of how nature works and why it exhibits certain characteristics. As a comparison, theories explain why we observe what we do and laws describe what happens.

For example, around the year 1800, Jacques Charles and other scientists were working with gases to, among other reasons, improve the design of the hot air balloon. These scientists found, after many, many tests, that certain patterns existed in the observations on gas behavior. If the temperature of the gas is increased, the volume of the gas increased. This is known as a natural law. A law is a relationship that exists between variables in a group of data. Laws describe the patterns we see in large amounts of data, but do not describe why the patterns exist.

What is a Belief?

A statement that is not scientifically provable. Beliefs may or may not be incorrect; they just are outside the realm of science to explore.

Laws vs. Theories

A common misconception is that scientific theories are rudimentary ideas that will eventually graduate into scientific laws when enough data and evidence has been accumulated. A theory does not change into a scientific law with the accumulation of new or better evidence. Remember, theories are explanations and laws are patterns we see in large amounts of data, frequently written as an equation. A theory will always remain a theory; a law will always remain a law.

Video \(\PageIndex{1}\): What’s the difference between a scientific law and theory?

• A hypothesis is a tentative explanation that can be tested by further investigation.
• A theory is a well-supported explanation of observations.
• A scientific law is a statement that summarizes the relationship between variables.
• An experiment is a controlled method of testing a hypothesis.

Contributors and Attributions

Marisa Alviar-Agnew  ( Sacramento City College )

Henry Agnew (UC Davis)

1.2 The Scientific Methods

Section learning objectives.

By the end of this section, you will be able to do the following:

• Explain how the methods of science are used to make scientific discoveries
• Define a scientific model and describe examples of physical and mathematical models used in physics
• Compare and contrast hypothesis, theory, and law

Teacher Support

The learning objectives in this section will help your students master the following standards:

• (A) know the definition of science and understand that it has limitations, as specified in subsection (b)(2) of this section;
• (B) know that scientific hypotheses are tentative and testable statements that must be capable of being supported or not supported by observational evidence. Hypotheses of durable explanatory power which have been tested over a wide variety of conditions are incorporated into theories;
• (C) know that scientific theories are based on natural and physical phenomena and are capable of being tested by multiple independent researchers. Unlike hypotheses, scientific theories are well-established and highly-reliable explanations, but may be subject to change as new areas of science and new technologies are developed;
• (D) distinguish between scientific hypotheses and scientific theories.

Section Key Terms

[OL] Pre-assessment for this section could involve students sharing or writing down an anecdote about when they used the methods of science. Then, students could label their thought processes in their anecdote with the appropriate scientific methods. The class could also discuss their definitions of theory and law, both outside and within the context of science.

[OL] It should be noted and possibly mentioned that a scientist , as mentioned in this section, does not necessarily mean a trained scientist. It could be anyone using methods of science.

Scientific Methods

Scientists often plan and carry out investigations to answer questions about the universe around us. These investigations may lead to natural laws. Such laws are intrinsic to the universe, meaning that humans did not create them and cannot change them. We can only discover and understand them. Their discovery is a very human endeavor, with all the elements of mystery, imagination, struggle, triumph, and disappointment inherent in any creative effort. The cornerstone of discovering natural laws is observation. Science must describe the universe as it is, not as we imagine or wish it to be.

We all are curious to some extent. We look around, make generalizations, and try to understand what we see. For example, we look up and wonder whether one type of cloud signals an oncoming storm. As we become serious about exploring nature, we become more organized and formal in collecting and analyzing data. We attempt greater precision, perform controlled experiments (if we can), and write down ideas about how data may be organized. We then formulate models, theories, and laws based on the data we have collected, and communicate those results with others. This, in a nutshell, describes the scientific method that scientists employ to decide scientific issues on the basis of evidence from observation and experiment.

An investigation often begins with a scientist making an observation . The scientist observes a pattern or trend within the natural world. Observation may generate questions that the scientist wishes to answer. Next, the scientist may perform some research about the topic and devise a hypothesis . A hypothesis is a testable statement that describes how something in the natural world works. In essence, a hypothesis is an educated guess that explains something about an observation.

[OL] An educated guess is used throughout this section in describing a hypothesis to combat the tendency to think of a theory as an educated guess.

Scientists may test the hypothesis by performing an experiment . During an experiment, the scientist collects data that will help them learn about the phenomenon they are studying. Then the scientists analyze the results of the experiment (that is, the data), often using statistical, mathematical, and/or graphical methods. From the data analysis, they draw conclusions. They may conclude that their experiment either supports or rejects their hypothesis. If the hypothesis is supported, the scientist usually goes on to test another hypothesis related to the first. If their hypothesis is rejected, they will often then test a new and different hypothesis in their effort to learn more about whatever they are studying.

Scientific processes can be applied to many situations. Let’s say that you try to turn on your car, but it will not start. You have just made an observation! You ask yourself, "Why won’t my car start?" You can now use scientific processes to answer this question. First, you generate a hypothesis such as, "The car won’t start because it has no gasoline in the gas tank." To test this hypothesis, you put gasoline in the car and try to start it again. If the car starts, then your hypothesis is supported by the experiment. If the car does not start, then your hypothesis is rejected. You will then need to think up a new hypothesis to test such as, "My car won’t start because the fuel pump is broken." Hopefully, your investigations lead you to discover why the car won’t start and enable you to fix it.

A model is a representation of something that is often too difficult (or impossible) to study directly. Models can take the form of physical models, equations, computer programs, or simulations—computer graphics/animations. Models are tools that are especially useful in modern physics because they let us visualize phenomena that we normally cannot observe with our senses, such as very small objects or objects that move at high speeds. For example, we can understand the structure of an atom using models, without seeing an atom with our own eyes. Although images of single atoms are now possible, these images are extremely difficult to achieve and are only possible due to the success of our models. The existence of these images is a consequence rather than a source of our understanding of atoms. Models are always approximate, so they are simpler to consider than the real situation; the more complete a model is, the more complicated it must be. Models put the intangible or the extremely complex into human terms that we can visualize, discuss, and hypothesize about.

Scientific models are constructed based on the results of previous experiments. Even still, models often only describe a phenomenon partially or in a few limited situations. Some phenomena are so complex that they may be impossible to model them in their entirety, even using computers. An example is the electron cloud model of the atom in which electrons are moving around the atom’s center in distinct clouds ( Figure 1.12 ), that represent the likelihood of finding an electron in different places. This model helps us to visualize the structure of an atom. However, it does not show us exactly where an electron will be within its cloud at any one particular time.

As mentioned previously, physicists use a variety of models including equations, physical models, computer simulations, etc. For example, three-dimensional models are often commonly used in chemistry and physics to model molecules. Properties other than appearance or location are usually modelled using mathematics, where functions are used to show how these properties relate to one another. Processes such as the formation of a star or the planets, can also be modelled using computer simulations. Once a simulation is correctly programmed based on actual experimental data, the simulation can allow us to view processes that happened in the past or happen too quickly or slowly for us to observe directly. In addition, scientists can also run virtual experiments using computer-based models. In a model of planet formation, for example, the scientist could alter the amount or type of rocks present in space and see how it affects planet formation.

Scientists use models and experimental results to construct explanations of observations or design solutions to problems. For example, one way to make a car more fuel efficient is to reduce the friction or drag caused by air flowing around the moving car. This can be done by designing the body shape of the car to be more aerodynamic, such as by using rounded corners instead of sharp ones. Engineers can then construct physical models of the car body, place them in a wind tunnel, and examine the flow of air around the model. This can also be done mathematically in a computer simulation. The air flow pattern can be analyzed for regions smooth air flow and for eddies that indicate drag. The model of the car body may have to be altered slightly to produce the smoothest pattern of air flow (i.e., the least drag). The pattern with the least drag may be the solution to increasing fuel efficiency of the car. This solution might then be incorporated into the car design.

Using Models and the Scientific Processes

Be sure to secure loose items before opening the window or door.

In this activity, you will learn about scientific models by making a model of how air flows through your classroom or a room in your house.

• One room with at least one window or door that can be opened
• Work with a group of four, as directed by your teacher. Close all of the windows and doors in the room you are working in. Your teacher may assign you a specific window or door to study.
• Before opening any windows or doors, draw a to-scale diagram of your room. First, measure the length and width of your room using the tape measure. Then, transform the measurement using a scale that could fit on your paper, such as 5 centimeters = 1 meter.
• Your teacher will assign you a specific window or door to study air flow. On your diagram, add arrows showing your hypothesis (before opening any windows or doors) of how air will flow through the room when your assigned window or door is opened. Use pencil so that you can easily make changes to your diagram.
• On your diagram, mark four locations where you would like to test air flow in your room. To test for airflow, hold a strip of single ply tissue paper between the thumb and index finger. Note the direction that the paper moves when exposed to the airflow. Then, for each location, predict which way the paper will move if your air flow diagram is correct.
• Now, each member of your group will stand in one of the four selected areas. Each member will test the airflow Agree upon an approximate height at which everyone will hold their papers.
• When you teacher tells you to, open your assigned window and/or door. Each person should note the direction that their paper points immediately after the window or door was opened. Record your results on your diagram.
• Did the airflow test data support or refute the hypothetical model of air flow shown in your diagram? Why or why not? Correct your model based on your experimental evidence.
• With your group, discuss how accurate your model is. What limitations did it have? Write down the limitations that your group agreed upon.
• Yes, you could use your model to predict air flow through a new window. The earlier experiment of air flow would help you model the system more accurately.
• Yes, you could use your model to predict air flow through a new window. The earlier experiment of air flow is not useful for modeling the new system.
• No, you cannot model a system to predict the air flow through a new window. The earlier experiment of air flow would help you model the system more accurately.
• No, you cannot model a system to predict the air flow through a new window. The earlier experiment of air flow is not useful for modeling the new system.

This Snap Lab! has students construct a model of how air flows in their classroom. Each group of four students will create a model of air flow in their classroom using a scale drawing of the room. Then, the groups will test the validity of their model by placing weathervanes that they have constructed around the room and opening a window or door. By observing the weather vanes, students will see how air actually flows through the room from a specific window or door. Students will then correct their model based on their experimental evidence. The following material list is given per group:

• One room with at least one window or door that can be opened (An optimal configuration would be one window or door per group.)
• Several pieces of construction paper (at least four per group)
• Strips of single ply tissue paper
• One tape measure (long enough to measure the dimensions of the room)
• Group size can vary depending on the number of windows/doors available and the number of students in the class.
• The room dimensions could be provided by the teacher. Also, students may need a brief introduction in how to make a drawing to scale.
• This is another opportunity to discuss controlled experiments in terms of why the students should hold the strips of tissue paper at the same height and in the same way. One student could also serve as a control and stand far away from the window/door or in another area that will not receive air flow from the window/door.
• You will probably need to coordinate this when multiple windows or doors are used. Only one window or door should be opened at a time for best results. Between openings, allow a short period (5 minutes) when all windows and doors are closed, if possible.

Answers to the Grasp Check will vary, but the air flow in the new window or door should be based on what the students observed in their experiment.

Scientific Laws and Theories

A scientific law is a description of a pattern in nature that is true in all circumstances that have been studied. That is, physical laws are meant to be universal , meaning that they apply throughout the known universe. Laws are often also concise, whereas theories are more complicated. A law can be expressed in the form of a single sentence or mathematical equation. For example, Newton’s second law of motion , which relates the motion of an object to the force applied ( F ), the mass of the object ( m ), and the object’s acceleration ( a ), is simply stated using the equation

Scientific ideas and explanations that are true in many, but not all situations in the universe are usually called principles . An example is Pascal’s principle , which explains properties of liquids, but not solids or gases. However, the distinction between laws and principles is sometimes not carefully made in science.

A theory is an explanation for patterns in nature that is supported by much scientific evidence and verified multiple times by multiple researchers. While many people confuse theories with educated guesses or hypotheses, theories have withstood more rigorous testing and verification than hypotheses.

[OL] Explain to students that in informal, everyday English the word theory can be used to describe an idea that is possibly true but that has not been proven to be true. This use of the word theory often leads people to think that scientific theories are nothing more than educated guesses. This is not just a misconception among students, but among the general public as well.

As a closing idea about scientific processes, we want to point out that scientific laws and theories, even those that have been supported by experiments for centuries, can still be changed by new discoveries. This is especially true when new technologies emerge that allow us to observe things that were formerly unobservable. Imagine how viewing previously invisible objects with a microscope or viewing Earth for the first time from space may have instantly changed our scientific theories and laws! What discoveries still await us in the future? The constant retesting and perfecting of our scientific laws and theories allows our knowledge of nature to progress. For this reason, many scientists are reluctant to say that their studies prove anything. By saying support instead of prove , it keeps the door open for future discoveries, even if they won’t occur for centuries or even millennia.

[OL] With regard to scientists avoiding using the word prove , the general public knows that science has proven certain things such as that the heart pumps blood and the Earth is round. However, scientists should shy away from using prove because it is impossible to test every single instance and every set of conditions in a system to absolutely prove anything. Using support or similar terminology leaves the door open for further discovery.

Check Your Understanding

• Models are simpler to analyze.
• Models give more accurate results.
• Models provide more reliable predictions.
• Models do not require any computer calculations.
• They are the same.
• A hypothesis has been thoroughly tested and found to be true.
• A hypothesis is a tentative assumption based on what is already known.
• A hypothesis is a broad explanation firmly supported by evidence.
• A scientific model is a representation of something that can be easily studied directly. It is useful for studying things that can be easily analyzed by humans.
• A scientific model is a representation of something that is often too difficult to study directly. It is useful for studying a complex system or systems that humans cannot observe directly.
• A scientific model is a representation of scientific equipment. It is useful for studying working principles of scientific equipment.
• A scientific model is a representation of a laboratory where experiments are performed. It is useful for studying requirements needed inside the laboratory.
• The hypothesis must be validated by scientific experiments.
• The hypothesis must not include any physical quantity.
• The hypothesis must be a short and concise statement.
• The hypothesis must apply to all the situations in the universe.
• A scientific theory is an explanation of natural phenomena that is supported by evidence.
• A scientific theory is an explanation of natural phenomena without the support of evidence.
• A scientific theory is an educated guess about the natural phenomena occurring in nature.
• A scientific theory is an uneducated guess about natural phenomena occurring in nature.
• A hypothesis is an explanation of the natural world with experimental support, while a scientific theory is an educated guess about a natural phenomenon.
• A hypothesis is an educated guess about natural phenomenon, while a scientific theory is an explanation of natural world with experimental support.
• A hypothesis is experimental evidence of a natural phenomenon, while a scientific theory is an explanation of the natural world with experimental support.
• A hypothesis is an explanation of the natural world with experimental support, while a scientific theory is experimental evidence of a natural phenomenon.

Use the Check Your Understanding questions to assess students’ achievement of the section’s learning objectives. If students are struggling with a specific objective, the Check Your Understanding will help identify which objective and direct students to the relevant content.

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Biology library

Course: biology library   >   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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What is a scientific hypothesis?

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

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

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

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

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

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

Here are some examples of hypothesis statements:

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

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

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

Types of scientific hypotheses

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

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

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

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

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

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

Scientific theory vs. scientific hypothesis

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

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

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

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

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

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

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

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

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

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

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

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

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Understanding Science

How science REALLY works...

• Understanding Science 101
• Misconceptions
• Testing ideas with evidence from the natural world is at the core of science.
• Scientific testing involves figuring out what we would  expect  to observe if an idea were correct and comparing that expectation to what we  actually  observe.
• Scientific arguments are built from an idea and the evidence relevant to that idea.
• Scientific arguments can be built in any order. Sometimes a scientific idea precedes any evidence relevant to it, and other times the evidence helps inspire the idea.

Misconception:  Science proves ideas.

Misconception:  Science can only disprove ideas.

Correction:  Science neither proves nor disproves. It accepts or rejects ideas based on supporting and refuting evidence, but may revise those conclusions if warranted by new evidence or perspectives.  Read more about it.

The core of science: Relating evidence and ideas

In this case, the term  argument  refers not to a disagreement between two people, but to an evidence-based line of reasoning — so scientific arguments are more like the closing argument in a court case (a logical description of what we think and why we think it) than they are like the fights you may have had with siblings. Scientific arguments involve three components: the idea (a  hypothesis  or theory), the  expectations  generated by that idea (frequently called predictions), and the actual observations relevant to those expectations (the evidence). These components are always related in the same logical way:

• What would we expect to see if this idea were true (i.e., what is our expected observation)?
• What do we actually observe?
• Do our expectations match our observations?

PREDICTIONS OR EXPECTATIONS?

When scientists describe their arguments, they frequently talk about their expectations in terms of what a hypothesis or theory predicts: “If it were the case that smoking causes lung cancer, then we’d  predict  that countries with higher rates of smoking would have higher rates of lung cancer.” At first, it might seem confusing to talk about a prediction that doesn’t deal with the future, but that refers to something going on right now or that may have already happened. In fact, this is just another way of discussing the expectations that the hypothesis or theory generates. So when a scientist talks about the  predicted  rates of lung cancer, he or she really means something like “the rates that we’d expect to see if our hypothesis were correct.”

If the idea generates expectations that hold true (are actually observed), then the idea is more likely to be accurate. If the idea generates expectations that don’t hold true (are not observed), then we are less likely to  accept  the idea. For example, consider the idea that cells are the building blocks of life. If that idea were true, we’d expect to see cells in all kinds of living tissues observed under a microscope — that’s our expected observation. In fact, we do observe this (our actual observation), so evidence supports the idea that living things are built from cells.

Though the structure of this argument is consistent (hypothesis, then expectation, then actual observation), its pieces may be assembled in different orders. For example, the first observations of cells were made in the 1600s, but cell theory was not postulated until 200 years later — so in this case, the evidence actually helped inspire the idea. Whether the idea comes first or the evidence comes first, the logic relating them remains the same.

Here, we’ll explore scientific arguments and how to build them. You can investigate:

Putting the pieces together: The hard work of building arguments

• Predicting the past
• Arguments with legs to stand on

Or just click the  Next  button to dive right in!

• Take a sidetrip
• Teaching resources

Scientific arguments rely on testable ideas. To learn what makes an idea testable, review our  Science Checklist .

• Forming hypotheses — scientific explanations — can be difficult for students. It is often easier for students to generate an expectation (what they think will happen or what they expect to observe) based on prior experience than to formulate a potential explanation for that phenomena. You can help students go beyond expectations to generate real, explanatory hypotheses by providing sentence stems for them to fill in: “I expect to observe A because B.” Once students have filled in this sentence you can explain that B is a hypothesis and A is the expectation generated by that hypothesis.
• You can help students learn to distinguish between hypotheses and the expectations generated by them by regularly asking students to analyze lecture material, text, or video. Students should try to figure out which aspects of the content were hypotheses and which were expectations.

Summing up the process

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Scientists help students vanquish research-experience Catch-22

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In new publication, CU Boulder scientists detail how the SkillsCenter allows students to gain credentials in basic to advanced research skills

It’s an unfortunate truth of higher education that students are not exempt from a classic Catch-22: You need research experience to gain research experience.

“Undergraduates participating in research is a key variable for enhancing their persistence in STEM professions,” explains Zachary Hazlett , a PhD candidate in the University of Colorado Boulder Department of Molecular, Cellular and Developmental Biology .  “But to gain access to opportunities in research is not the most straightforward. For a lot of students, these things aren’t baked into their undergraduate degree plan.”

So, students seeking research-focused internships, jobs or higher education opportunities after graduation are often inconsistently prepared with the necessary skills and experience. Hence, the SkillsCenter .

Zachary Hazlett, a PhD candidate in the University of Colorado Boulder Department of Molecular, Cellular and Developmental Biology, is a lead TA on the SkillsCenter proctor team and first author on a paper newly publish in Cell detailing the organizing philosophy, structure and goals of SkillsCenter.

As detailed in a paper newly published  in the journal Cell , the SkillsCenter is a modular research skills training course that allows students to “gain training and micro-credentials in the laboratory skills of their choosing.”

In other words, Hazlett says, “what if there was a bridge, something between the classroom and these research spaces that can allow students to gain that necessary experience? That can help equip them to enter those spaces both confidently and competently?”

Module-based curriculum

The SkillsCenter, which is open to students of every major, emerged, in part, from a recognition that undergraduate students have often gained research experiences “by cold-calling faculty members and saying, ‘I’d like to work in research, are there any opportunities in your lab?’” Hazlett says.

Understandably, faculty often ask what their previous experience is, and if a student doesn’t have any, they have to hope they’ll get lucky and find a faculty member willing to teach them.

So, faculty and graduate students in the Department of Molecular, Cellular and Developmental Biology, led by Professor Michael Stowell , began researching and discussing alternative means by which undergraduate students could gain the training and experience they need to gain these critical professional development opportunities.

Based on the principle of “learning by doing,” they designed a module-based curriculum in which modules are scaled by skill level, with appropriate prerequisites, and students can learn at their own self-directed pace. In fall 2021, the first 10 students enrolled in the for-credit SkillsCenter course, working through skills such as lab safety, pipette operation and calibration, centrifugation, buffers and stocks preparation, autoclave sterilization and more.

Today, the course offers training in the laboratory basics as well as advanced training techniques such as polymerase chain reaction, protein expression and purification and various forms of microscopy.

“The course has been designed very carefully,” Hazlett says. “We’ve done our best to build a laboratory space that mimics a traditional research space. Students working in the SkillsCenter gain the experience of what it would be like to be a member of a laboratory research group—in charge of maintaining their space, scheduling equipment, restocking materials, etc. The training modules themselves mimic something a trainee would encounter, with resources to help them and guide them in their conceptual understanding and procedural competence.”

Lab proctors—who are the course instructor, graduate students in the department and a number of undergraduate students who previously took the course—provide on- and off-site guidance for students and assess their work.

What if there was a bridge, something between the classroom and these research spaces that can allow students to gain that necessary experience? That can help equip them to enter those spaces both confidently and competently?"

Learning the scientific process

Through six semesters, SkillsCenter has grown and evolved from the original 10 students to nearly 100 per semester. The lab space is now open from 9 a.m. to 5 p.m. Monday through Friday thanks to increased staffing, and students can work on their modules when their schedule allows.

“It is very important that we have trained lab proctors, and that we instruct our students very carefully on how to engage in this course,” Hazlett says. “Students are instructed that they are responsible for seeking out the resources and guidance they need, and we make sure they know how to access the supports they need.”

Each module requires a certain number of tasks that students complete and submit to proctors for review. Proctors monitor students’ work through each module, give feedback and assess their progress through the scientific process—from hypothesis through notes and observations to interpretation of results.

After completing a module and passing all its required tasks, students receive a certificate for each skill, “so they can collect these certificates and put those skills on their resumes,” Hazlett says, adding that he and his colleagues are working with ORCiD and digital badge organizations to create digital credentials that students can display to future employers. “We also want to embed students’ raw data into those badges, so if an employer wants proof of their skills, they have direct evidence of students’ technical proficiencies.”

Hazlett and his colleagues also are building a network of industry and academic research lab partners to “create an ecosystem for training STEM students. Many students often excitedly explain to me how they have convinced faculty researchers to let them join their labs because of the experiences they have gained in the SkillsCenter.”

Researchers Beiyi Xu, Jennifer Knight, Michael Klymkowsky and Michael Stowell also contributed to the Cell publication.

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• Published: 05 June 2024

Post-January 6th deplatforming reduced the reach of misinformation on Twitter

• Stefan D. McCabe   ORCID: orcid.org/0000-0002-7180-145X 1   na1 ,
• Diogo Ferrari   ORCID: orcid.org/0000-0003-2454-0776 2   na1 ,
• Jon Green 3 ,
• David M. J. Lazer   ORCID: orcid.org/0000-0002-7991-9110 4 , 5 &
• Kevin M. Esterling   ORCID: orcid.org/0000-0002-5529-6422 2 , 6  

Nature volume  630 ,  pages 132–140 ( 2024 ) Cite this article

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The social media platforms of the twenty-first century have an enormous role in regulating speech in the USA and worldwide 1 . However, there has been little research on platform-wide interventions on speech 2 , 3 . Here we evaluate the effect of the decision by Twitter to suddenly deplatform 70,000 misinformation traffickers in response to the violence at the US Capitol on 6 January 2021 (a series of events commonly known as and referred to here as ‘January 6th’). Using a panel of more than 500,000 active Twitter users 4 , 5 and natural experimental designs 6 , 7 , we evaluate the effects of this intervention on the circulation of misinformation on Twitter. We show that the intervention reduced circulation of misinformation by the deplatformed users as well as by those who followed the deplatformed users, though we cannot identify the magnitude of the causal estimates owing to the co-occurrence of the deplatforming intervention with the events surrounding January 6th. We also find that many of the misinformation traffickers who were not deplatformed left Twitter following the intervention. The results inform the historical record surrounding the insurrection, a momentous event in US history, and indicate the capacity of social media platforms to control the circulation of misinformation, and more generally to regulate public discourse.

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Data availability.

Aggregate data used in the analysis are publicly available at the OSF project website ( https://doi.org/10.17605/OSF.IO/KU8Z4 ) to any researcher for purposes of reproducing or extending the analysis. The tweet-level data and specific user demographics cannot be publicly shared owing to privacy concerns arising from matching data to administrative records, data use agreements and platforms’ terms of service. Our replication materials include the code used to produce the aggregate data from the tweet-level data, and the tweet-level data can be accessed after signing a data-use agreement. For access requests, please contact D.M.J.L.

Code availability

All code necessary for reproduction of the results is available at the OSF project site https://doi.org/10.17605/OSF.IO/KU8Z4 .

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Acknowledgements

The authors thank N. Grinberg, L. Friedland and K. Joseph for earlier technical work on the development of the Twitter dataset. Earlier versions of this paper were presented at the Social Media Analysis Workshop, UC Riverside, 26 August 2022; at the Annual Meeting of the American Political Science Association, 17 September 2022; and at the Center for Social Media and Politics, NYU, 23 April 2021. Special thanks go to A. Guess for suggesting the DID analysis. D.M.J.L. acknowledges support from the William & Flora Hewlett Foundation and the Volkswagen Foundation. S.D.M. was supported by the John S. and James L. Knight Foundation through a grant to the Institute for Data, Democracy & Politics at the George Washington University.

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These authors contributed equally: Stefan D. McCabe, Diogo Ferrari

Authors and Affiliations

Institute for Data, Democracy & Politics, George Washington University, Washington, DC, USA

Stefan D. McCabe

Department of Political Science, University of California, Riverside, Riverside, CA, USA

Diogo Ferrari & Kevin M. Esterling

Department of Political Science, Duke University, Durham, NC, USA

Network Science Institute, Northeastern University, Boston, MA, USA

David M. J. Lazer

Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA

School of Public Policy, University of California, Riverside, Riverside, CA, USA

Kevin M. Esterling

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Contributions

The order of author listed here does not indicate level of contribution. Conceptualization of theory and research design: S.D.M., D.M.J.L., D.F., K.M.E. and J.G. Data curation: S.D.M. and J.G. Methodology: D.F. Visualization: D.F. Funding acquisition: D.M.J.L. Project administration: K.M.E., S.D.M. and D.M.J.L. Writing, original draft: K.M.E. and D.M.J.L. Writing, review and editing: K.M.E., D.F., S.D.M., D.M.J.L. and J.G.

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Correspondence to David M. J. Lazer .

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Extended data figures and tables

Extended data fig. 1 replication of the did results varying the number of deplatformed accounts..

DID estimates where the intervention depends on the number of deplatformed users that were followed by the not-deplatformed misinformation sharers. Results are two-way fixed effect point estimates (dots) and 95% confidence intervals (bars) of the difference-in-differences for all activity levels combined. Estimates use ordinary least squares with clustered standard errors at user-level. The Figure shows results including and excluding Trump followers (color code). The x-axis shows the minimum number of deplatformed accounts the user followed from at least one (1+) to at least ten (10+). Total sample sizes for each dosage level: Follow Trump (No): 1: 625,865; 2: 538,460; 3: 495,723; 4: 470,380; 5: 451,468; 6: 437,574; 7: 426,772; 8: 417,200; 9: 408,672; 10: 401,467; Follow Trump (Yes): 1: 688,174; 2: 570,637; 3: 514,352; 4: 481,684; 5: 460,676; 6: 444,656; 7: 432,659; 8: 421,924; 9: 413,241; 10: 405,766.

Extended Data Fig. 2 SRD results for total (bottom row) and average (top row) misinformation tweets and retweets, for deplatformed and not-deplatformed users.

Sample size includes 546 observations (days) on average across groups (x-axis), 404 before and 136 after. The effective number of observations is 64.31 days before and after on average. The estimation excludes data between Jan 6 (cutoff point) and 12 (included). January 6th is the score value 0, and January 12th the score value 1. Optimal bandwidth of 32.6 days with triangular kernel and order-one polynomial. Bars indicate 95% robust bias-corrected confidence intervals.

Extended Data Fig. 3 Time series of the daily mean of non-misinformation URL sharing.

Degree five polynomial regression (fitted line) before and after the deplatforming intervention, separated by subgroup (panel rows), for liberal-slant news (right column), and conservative-slant news (left column) sharing activity. Shaded area around the fitted line is the 95% confidence interval of the fitted values. As a placebo test we evaluate the effect of the intervention on sharing non-fake news for each of our subgroups. Since sharing non-misinformation does not violate Twitter’s Civic Integrity policy – irrespective of the ideological slant of the news – we do not expect the intervention to have an impact on this form of Twitter engagement; see SI for how we identify liberal and conservative slant of these domains from ref. 52 . Among the subgroups, users typically did not change their sharing of liberal or conservative non-fake news. Taking these results alongside those in Fig. 2 implies that these subgroups of users did not substitute non-misinformation conservative news sharing during and after the insurrection in place of misinformation.

Extended Data Fig. 4 Time series of misinformation tweets and retweets (panel columns), separately for high, medium and low activity users (panel rows).

Fitted straight lines describe a linear regression fitted using ordinary least squares of daily total misinformation retweeted standardized (y-axis) on days (x-axis) before January 6th and after January 12th. Shaded areas around the fitted line are 95% confidence intervals.

Extended Data Fig. 5 Replicates Fig. 5 but with adjustment covariates.

Corresponding regression tables are Supplementary Information Tables 1 to 3 . Two-way fixed effect point estimates (dots) and 95% confidence intervals (bars) of the difference-in-differences for high, moderate, and low activity users, as well as all these levels combined (x-axis). P-values (stars) are from two-sided t-tests based on ordinary least squares estimates with clustered standard errors at user-level. Estimates compare followers (treated group) and not-followers (reference group) of deplatformed users after January 12th (post-treatment period) and before January 6th (pre-treatment period). No multiple test correction was used. See Supplementary Information Tables 1 – 3 for exact values with all activity level users combined. Total sample sizes of not-followers (reference) and Trump-only followers: combined: 306,089, high: 53,962, moderate: 219,375, low: 32,003; Followers: combined: 662,216, high: 156,941, moderate: 449,560, low: 53,442; Followers (4+): combined: 463,176, high: 115,264, moderate: 302,907, low: 43,218.

Extended Data Fig. 6 Placebo test of SRD results for total (bottom row) and average (top row) shopping and sports tweets and retweets at the deplatforming intervention, among those not deplatformed.

Sample size includes 545 observations (days), 404 before the intervention and 141 after. Optimal bandwidth of 843.6 days with triangular kernel and order-one polynomial. Cutoff points on January 6th (score 0) and January 12th (score 1). Bars indicate 95% robust bias-corrected confidence intervals. These are placebo tests since tweets about sports and shoppings should not be affected by the insurrection or deplatforming.

Extended Data Fig. 7 Placebo test of SRD results for total (bottom row) and average (top row) misinformation tweets and retweets using December 20th as an arbitrary cutoff point.

Sample size includes 551 observations (days), 387 before the intervention and 164 after. Optimal bandwidth of 37.2 days with triangular kernel and order-one polynomial. Bars indicate 95% robust bias-corrected confidence intervals about the SRD coefficients. This is a placebo test of the intervention period.

Extended Data Fig. 8 Placebo test of SRD results for total (bottom row) and average (top row) misinformation tweets and retweets using January 18th as a cutoff point.

The parameters are very similar to Extended Data Fig. 7 .

Supplementary information

Supplementary information.

Supplementary Figs. 1–5 provide descriptive information about our subgroups, a replication of the panel data using the Decahose, and robustness analyses for the SRD. Supplementary Tables 1–5 show full parameter estimates for the DID models, summary statistics for follower type and activity level, and P values for the DID analyses under different multiple comparisons corrections.

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McCabe, S.D., Ferrari, D., Green, J. et al. Post-January 6th deplatforming reduced the reach of misinformation on Twitter. Nature 630 , 132–140 (2024). https://doi.org/10.1038/s41586-024-07524-8

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Received : 27 October 2023

Accepted : 06 May 2024

Published : 05 June 2024

Issue Date : 06 June 2024

DOI : https://doi.org/10.1038/s41586-024-07524-8

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