Hypothesis

A **hypothesis** is an idea or guess that tries to explain something that happens in the world. Scientists make hypotheses when they notice something and want to understand why it happens. For example, if plants near a window grow faster than plants in the shade, a scientist might make a hypothesis that “plants grow faster when they get more sunlight.” A good scientific hypothesis is not just a random guess, it is based on observations (things you can see, measure, or record) and can be tested through experiments. This means other people should be able to do the same experiment and get similar results if the hypothesis is correct.

If many experiments show that the hypothesis is true again and again, it can become a scientific theory. A theory isn’t a “guess” in science, it is a well-tested explanation that has strong evidence behind it. For example, the theory of gravity or the theory of evolution started out as hypotheses but became theories after years of testing and proof. In everyday language, people sometimes mix up “hypothesis” and “theory,” using both to mean a “guess.” But in science, they mean very different things. A hypothesis is an idea that’s being tested, while a theory is an explanation that’s already been tested many times and found to be true.

A working hypothesis is a temporary idea that scientists use to help them move forward in their research. It is like a rough guess that gives them a starting point to explore and test new ideas. Scientists know that this guess might not be completely right, or even might be wrong, but it helps them make progress instead of getting stuck. For example, imagine scientists are trying to understand why bees are disappearing. They might start with a working hypothesis like, “Bees are dying because of a new kind of pesticide.” Even if they are not sure it is true, this idea gives them something to test. They can then collect data, do experiments, and see what they find. Working hypotheses are often changed or thrown away once scientists learn more. That is normal. The goal is not to be right the first time, but to use the hypothesis as a tool for discovery. It helps point scientists in the right direction, especially when they are brainstorming or trying to solve a difficult problem.

In formal logic, a hypothesis is the first part of an “if–then” statement. For example, in the sentence “If it rains, then the ground will get wet,” the part “If it rains” is the hypothesis. It’s also called the antecedent, and it is the condition that might cause something else to happen. The second part, “then the ground will get wet,” is called the consequent, it is what happens because of the first part. You can think of it like a cause-and-effect relationship. The hypothesis is the cause, and the consequent is the effect. Sometimes, the hypothesis talks about something that might not actually happen, it is just a “what if” situation. For example, “If humans could fly, then cars wouldn’t be as important.” Even though humans cannot fly, it is still a valid logical statement used to explore ideas. The word “hypothetical” describes something that is based on a hypothesis or an assumption. So, a hypothetical situation is one that we imagine or suppose, even if it is not real, just to think about what would happen if it were true.

## History

A long time ago, the word hypothesis did not mean what it means today. In ancient times, it was used to describe a summary of the plot of a classical play or drama. For example, if someone wanted to tell you what a Greek play was about before you watched it, they might give you the hypothesis, a short explanation of the story. The English word hypothesis comes from the ancient Greek word “ὑπόθεσις” (hypothesis), which literally means “putting or placing under.” Over time, the meaning expanded to include the idea of a supposition, or something that is assumed to be true for the sake of discussion or testing. That is how we got the modern scientific meaning of the word, a hypothesis is now an idea “placed under” investigation to see if it holds up to testing and evidence.

In Plato’s dialogue called Meno, the philosopher Socrates talks about using a method called “investigating from a hypothesis.” This means starting with a smart idea or possible explanation to help solve a problem, kind of like how mathematicians use shortcuts or clever tricks to make hard problems easier to handle. In this older sense, a hypothesis did not have to be a proven truth, it was just a useful idea to help guide thinking or calculations.

A famous example of this older meaning comes from the 1600s, when Cardinal Robert Bellarmine warned the scientist Galileo Galilei not to say that the Earth really moves around the Sun, but instead to treat that idea as just a hypothesis. In other words, Galileo could use the idea to make predictions and explain the stars, but he was not supposed to claim it was true. Back then, “hypothesis” meant more like a helpful theory or model, something useful for thinking and working with, even if people did not fully accept it as reality.

Today, a hypothesis refers to an idea that needs to be tested. A hypothesis needs more work by the researcher in order to check it. A tested hypothesis that works may become part of a theory, or become a theory itself. The testing should be an attempt to prove that the hypothesis is wrong. That is, there should be a way to falsify the hypothesis, at least in principle if not in practice.

People often call a hypothesis an "educated guess".
- "When it is not clear under which law of nature an effect or class of effect belongs, we try to fill this gap by means of a guess. Such guesses have been given the name _conjectures_ or _hypotheses_". Hans Christian Ørsted (1811)
- "In general we look for a new law by the following process. First we guess it. ..."

Experimenters may test and reject several hypotheses, before solving the problem or reaching a satisfactory theory.

A 'working hypothesis' is just a rough kind of hypothesis that is provisionally accepted as a basis for further research. The hope is that a theory will be produced, even if the hypothesis ultimately fails.

Hypotheses are especially important in science. Several philosophers have said that without hypotheses, there could be no science. In recent years, philosophers of science have tried to integrate the various approaches to testing hypotheses (and the scientific method in general), to form a more complete system. The point is that hypotheses are suggested ideas, which are then tested by experiments or observations.

## Uses

In the 21st century, the word hypothesis usually means an idea or explanation that still needs to be tested to see if it is true. It is like a temporary answer to a question that scientists or researchers come up with before they do experiments. For example, if someone thinks that “listening to music helps students concentrate better,” that is a hypothesis. They will need to test it to find out if it is actually true. When scientists make a hypothesis, they must be specific about what they mean. They often describe their ideas in operational terms, which means explaining exactly how they will measure or test something. For example, instead of saying “students focus better,” they might say “students who listen to music will score higher on a concentration test.” This makes the hypothesis clear and testable.

Scientists then do research and experiments to confirm (prove) or disprove (show false) their hypothesis. If many tests and experiments keep showing that the hypothesis is correct, it might become part of a scientific theory, or in rare cases, become a theory itself, like how the theory of gravity started from simple observations and ideas. Sometimes, hypotheses can be written as mathematical models, which use equations to describe how things work. Other times, they can be written as statements about what exists or happens. For example, a simple statement might say, “This type of bacteria grows faster in warm water.” A more general one might say, “All bacteria grow faster in warm water.” Scientists often move between specific cases and general rules when testing and forming hypotheses.

In business and entrepreneurship, a hypothesis is a temporary idea that helps people test what might work for their product or business. It is like making an educated guess about what customers want or how a company can succeed. For example, an entrepreneur might have a hypothesis that “people will buy more smoothies if they are sold in reusable bottles.” Once they come up with this idea, they test it to see if it is true or false. They might run an experiment, like selling smoothies in both regular cups and reusable bottles to compare which sells better. If customers really do prefer the reusable bottles, the hypothesis is proven true. If not, it is proven false, and they learn something useful for improving their business.

A good hypothesis should help scientists make predictions, which are educated guesses about what will happen next. These predictions can be tested through reasoning (logical thinking) or experiments. For example, if a scientist has a hypothesis that “plants grow faster in sunlight,” they can predict that “if I put a plant in sunlight, it will grow taller than one kept in the dark.” Sometimes, these predictions involve statistics, meaning they deal with chances or probabilities, like “there’s a 70% chance this medicine will reduce headaches.”

The philosopher Karl Popper said that for an idea to be truly scientific, it must be falsifiable, which means there must be a way to prove it wrong if it is not true. For example, the statement “all swans are white” can be proven false if someone finds just one black swan. If an idea cannot be tested or proven wrong, it is not considered a scientific hypothesis. Some other philosophers disagreed with Popper’s view and suggested different ways to judge scientific ideas. Some believed in verifiability, which means proving something true through evidence, while others supported coherence, which means seeing how well an idea fits with everything else we already know.

In science, the main way to test a hypothesis is through experimentation, doing tests to see if the results match the prediction. Simply observing things without testing often does not help answer deep questions. Sometimes, when real experiments are not possible, scientists use thought experiments, imaginary “what if” situations, to explore whether an idea makes sense. For example, Einstein often used thought experiments when developing his theories about space and time.

When a scientist or researcher makes a hypothesis, they should not already know the answer to the question they are testing. The whole point of a hypothesis is to find out something new. For example, if a scientist already knows that “salt makes ice melt,” there is no reason to test it again. It is no longer a hypothesis, just a known fact. A hypothesis is useful only when the outcome is still unknown or still being studied. That way, experiments and tests can help increase the chances of finding out whether the hypothesis is true or false. If the researcher already knows what will happen, then any result is just a consequence, not a discovery. The researcher should have thought about those consequences before even making the hypothesis.

Sometimes, a hypothesis cannot be tested right away because we do not have the right tools or technology. In that case, it can later be tested by other scientists who make new observations or invent new methods. For example, before telescopes were powerful enough, people could not test many ideas about space, but once better technology was developed, those old hypotheses could finally be tested through new experiments and observations.

## Hypotheses, concepts and measurement

In science, concepts are the building blocks of ideas. A concept is simply something scientists want to study or measure, like temperature, speed, or growth. Scientists use these concepts to form hypotheses, which are testable statements that show how different things might be connected. For example, a scientist might suggest, “If the temperature goes up, then plant growth will increase.” This links the two concepts, temperature and growth, in a way that can be tested through experiments.

When scientists collect several related hypotheses, they group them together into what’s called a conceptual framework. This framework helps them see the bigger picture of how different ideas fit together. Over time, if this framework becomes more detailed and explains not just what happens but why it happens, it can grow into a scientific theory. For instance, many smaller ideas and experiments about how objects move and fall helped form the larger theory of gravity, which explains how everything with mass pulls on everything else.

The philosopher Carl Gustav Hempel explained that a theory only becomes truly scientific when it can be tested using real-world evidence. This means that even though theories often deal with ideas we cannot see directly, like atoms or gravity, they still need to connect to things we can observe or measure. For example, scientists cannot see gravity itself, but they can watch an apple fall from a tree or a planet orbit a star. These visible effects are how they test the theory.

To make these connections clear, scientists use what Hempel called rules of interpretation. These are like strings that tie the “floating” world of theory to the “ground” of real-world observation. Imagine a big web of scientific ideas floating above the ground, this is the theory. The strings that hold it down are the rules that connect those ideas to things we can measure or see. For instance, the theory of gravity is connected to falling objects, tides, and planetary motion through these “strings” of observation and measurement.

When scientists define the ideas in a theory clearly enough that they can be measured or tested, those ideas become derived hypotheses. These are smaller, specific predictions that come from the larger theory. For example, from the theory of gravity, a scientist might form the testable hypothesis: “A ball will fall faster on Earth than on the Moon.” If experiments or data support this hypothesis, it strengthens the theory of gravity. But if the data does not match, the theory might need to be revised.

In real life, building a theory and testing it usually happen together. As scientists develop theories, they also think about how to test them in the real world. Hempel suggested, though, that it is helpful to imagine these as two separate steps: first, creating the theory to explain something, and then finding ways to connect that theory to observations and experiments. This way, science stays grounded in reality while still exploring the unseen ideas that explain how the world works.

## Statistics

In statistics, people talk about correlation: correlation is how closely related two events or phenomena are. A proposition (or hypothesis) that two events are related cannot be tested in the same way as a law of nature can be tested. An example would be to see if some drug is effective to treat a given medical condition. Even if there is a strong correlation that indicates that this is the case, some samples would still not fit the hypothesis.

There are two hypotheses in statistical tests, called the null hypothesis, often written as , and the alternative hypothesis, often written as . The null hypothesis states that there is no link between the phenomena, and is usually assumed to be true until it can be proven wrong beyond a reasonable doubt. The alternative hypothesis states that there _is_ some kind of link. It is usually the opposite of the null hypothesis, and is what one would conclude if null hypothesis is rejected. The alternative hypothesis may take several forms. It can be two-sided (for example: there is _some_ effect, in a yet unknown direction) or one-sided (the direction of the supposed relation, positive or negative, is fixed in advance).

### Statistical hypothesis testing

When scientists study whether two things are connected, like whether a new medicine actually helps cure a disease, they use a different kind of testing than when they study laws of nature, like gravity. In this case, the hypothesis is something like “this medicine helps people get better.” If a few patients do not get better after taking the medicine, that doesn’t automatically mean the hypothesis is wrong. People’s bodies are different, and there can be many reasons why the medicine did not work for everyone. Instead of judging from a few examples, scientists use statistics, a way of looking at large amounts of data to find patterns. They test how likely it is that the results they are seeing could happen by chance if the medicine did not really work. If that likelihood is very small, say, less than 1%, scientists assume the medicine probably does have an effect. But if the likelihood is high, then the results might just be random, meaning the medicine might not really be helping.

In statistical hypothesis testing, scientists or researchers compare two different ideas to see which one fits the data better. These two ideas are called the null hypothesis and the alternative hypothesis. The null hypothesis says that nothing special is happening, there is no real connection or effect between the things being studied. For example, if scientists are testing a new medicine, the null hypothesis would say, “This medicine does not help people get better more than a placebo (a fake pill).” It’s like saying, “There’s no difference.” The alternative hypothesis is the opposite idea. It says there is some kind of effect or relationship. In the same example, it would say, “This medicine helps people recover faster than a placebo.” Scientists test both ideas using data to see which one the evidence supports. There are two main types of alternative hypotheses. A two-sided hypothesis means scientists do not know which direction the effect will go, just that there is a difference (for example, “the medicine changes recovery time, but we do not know if it makes it longer or shorter”). A one-sided hypothesis means they already have a specific direction in mind (for example, “the medicine helps people recover faster”).

When scientists test a hypothesis using statistics, they have to decide how sure they need to be before they say their results are real and not just due to chance. This is called the significance level. It tells them the acceptable risk of making a mistake, specifically, the risk of rejecting the null hypothesis (saying there is an effect) when it is actually true. The most common significance levels are 0.10, 0.05, and 0.01. These numbers represent probabilities. For example:
- A level of 0.10 means there’s a 10% chance of being wrong.
- A level of 0.05 means there’s a 5% chance of being wrong.
- A level of 0.01 means there’s only a 1% chance of being wrong.

So, if scientists use a 0.05 significance level, they are saying they are 95% confident that their result is real and not just random chance. It is very important that scientists choose their significance level before they start collecting or looking at their data. This rule keeps the test fair and honest. If someone changes the rule after seeing the results (for example, lowering the level to make their results look more convincing), then the test becomes invalid, it cannot be trusted.

When scientists test a hypothesis, the number of people or things they study, called the sample size, is very important. The sample size affects how accurate and trustworthy the results will be. If the sample size is too small, the results might not show the real pattern, even if one exists. For example, if you only test a new medicine on five people, you might not get enough information to know whether it truly works or not. To avoid this problem, scientists plan ahead and choose a large enough sample size before starting the study. Scientists also think about something called effect size, which measures how big or strong the difference or relationship is between things being studied. For example, if a new teaching method improves students’ grades only a little, that’s a small effect size. If it improves them a lot, that is a large effect size. It is best to plan for small, medium, and large effect sizes before running the experiment. This helps researchers decide how many participants they will need to get clear, reliable results for the type of test they are doing.

## Honours

Mount Hypothesis in Antarctica is named in appreciation of the role of hypotheses in scientific research.

## Related pages

- Falsifiability
- Gaia hypothesis
- Logic
- Null hypothesis
- Occam's razor
- Postulate
- Statistical hypothesis test
- Thought experiment

## Other websites

- Research and evaluation glossary
- Analysis and synthesis - on scientific method, based on a study by Bernhard Riemann from the Swedish Morphological Society