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What Is a Scientific Hypothesis?
A scientific hypothesis is 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, the definition can be expanded. A hypothesis also includes an explanation of why the guess may be correct, according to National Science Teachers Association.
A hypothesis is a suggested solution for an unexplained occurrence that does not fit into current accepted scientific theory. The basic idea of a hypothesis is that there is no pre-determined outcome. For a hypothesis to be termed a scientific hypothesis, it has to be something that can be supported or refuted through carefully crafted experimentation or observation. This is called falsifiability and testability, an idea that was advanced in the mid-20th century a British philosopher named Karl Popper, according to the Encyclopedia Britannica.
A key function in this step in the scientific method is deriving predictions from the hypotheses about the results of future experiments, and then performing those experiments to see whether they support the predictions.
A hypothesis is usually written in the form of an if/then statement, according to the University of California. This statement gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include “may.”
The evolution of a hypothesis
Most formal hypotheses consist of concepts that can be connected and their relationships tested. A group of hypotheses comes together to form a conceptual framework. As sufficient data and evidence are gathered to support a hypothesis, it becomes a working hypothesis, which is a milestone on the way to becoming a theory. Though hypotheses and theories are often confused, theories are the result of a tested hypothesis. While hypotheses are ideas, theories explain the findings of the testing of those ideas.
“Theories are the ways that we make sense of what we observe in the natural world. Theories are structures of ideas that explain and interpret facts,” said Tanner.
A hypothesis can’t be right unless it can be proven wrong
A hypothesis is the cornerstone of the scientific method.
It is an educated guess about how the world works that integrates knowledge with observation.
Everyone appreciates that a hypothesis must be testable to have any value, but there is a much stronger requirement that a hypothesis must meet.
A hypothesis is considered scientific only if there is the possibility to disprove the hypothesis.
The proof lies in being able to disprove
A hypothesis or model is called falsifiable if it is possible to conceive of an experimental observation that disproves the idea in question. That is, one of the possible outcomes of the designed experiment must be an answer, that if obtained, would disprove the hypothesis.
Our daily horoscopes are good examples of something that isn’t falsifiable. A scientist cannot disprove that a Piscean may get a surprise phone call from someone he or she hasn’t heard from in a long time. The statement is intentionally vague. Even if our Piscean didn’t get a phone call, the prediction cannot be false because he or she may get a phone call. They may not.
A good scientific hypothesis is the opposite of this. If there is no experimental test to disprove the hypothesis, then it lies outside the realm of science.
Scientists all too often generate hypotheses that cannot be tested by experiments whose results have the potential to show that the idea is false.
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Three types of experiments proposed by scientists
- Type 1 experiments are the most powerful. Type 1 experimental outcomes include a possible negative outcome that would falsify, or refute, the working hypothesis. It is one or the other.
- Type 2 experiments are very common, but lack punch. A positive result in a type 2 experiment is consistent with the working hypothesis, but the negative or null result does not address the validity of the hypothesis because there are many explanations for the negative result. These call for extrapolation and semantics.
- Type 3 experiments are those experiments whose results may be consistent with the hypothesis, but are useless because regardless of the outcome, the findings are also consistent with other models. In other words, every result isn’t informative.
Formulate hypotheses in such a way that you can prove or disprove them by direct experiment.
Science advances by conducting the experiments that could potentially disprove our hypotheses.
Increase the efficiency and impact of your science by testing clear hypotheses with well-designed experiments.
Testing a hypothesis
Notice that all of the statements, above, are testable. The primary trait of a hypothesis is that something can be tested and that those tests can be replicated, according to Midwestern State University.
An example of untestable statement is, “All people fall in love at least once.” The definition of love is subjective. Also, it would be impossible to poll every human about their love life. An untestable statement can be reworded to make it testable, though. For example, the previous statement could be changed to, “If love is an important emotion, some may believe that everyone should fall in love at least once.” With this statement, the researcher can poll a group of people to see how many believe people should fall in love at least once.
A hypothesis is often examined by multiple scientists to ensure the integrity and veracity of the experiment. This process can take years, and in many cases hypotheses do not go any further in the scientific method as it is difficult to gather sufficient supporting evidence.
“As a field biologist my favorite part of the scientific method is being in the field collecting the data,” Jaime Tanner, a professor of biology at Marlboro College, told Live Science. “But what really makes that fun is knowing that you are trying to answer an interesting question, so the first step in identifying questions and generating possible answers (hypotheses) is also very important and is a creative process. Then once you collect the data you analyze it to see if your hypothesis is supported or not.”
A null hypothesis is the name given to a hypothesis that is possibly false or has no effect. Often, during a test, the scientist will study another branch of the idea that may work, which is called an alternative hypothesis, according to the University of California, Berkeley.
During a test, the scientist may try to prove or disprove just the null hypothesis or test both the null and the alternative hypothesis. If a hypothesis specifies a certain direction, it is called one-tailed hypothesis. This means that the scientist believes that the outcome will be either with effect or without effect. When a hypothesis is created with no prediction to the outcome, it is called a two-tailed hypothesis because there are two possible outcomes. The outcome could be with effect or without effect, but until the testing is complete, there is no way of knowing which outcome it will be, according to the Web Center for Social Research Methods.
During testing, a scientist may come upon two types of errors. A Type I error is when the null hypothesis is rejected when it is true. A Type II error occurs when the null hypothesis is not rejected when it is false, according to the University of California, Berkeley.
Upon analysis of the results, a hypothesis can be rejected or modified, but it can never be proven to be correct 100 percent of the time. For example, relativity has been tested many times, so it is generally accepted as true, but there could be an instance, which has not been encountered, where it is not true. For example, a scientist can form a hypothesis that a certain type of tomato is red. During research, the scientist then finds that each tomato of this type is red. Though his findings confirm his hypothesis, there may be a tomato of that type somewhere in the world that isn’t red. Thus, his hypothesis is true, but it may not be true 100 percent of the time.