Hypothesis research tries to determine what is happening in some situation by evaluating various conjectures. The goal is to determine which hypothesis is most likely to be true. A scientific hypothesis is the initial building block in the scientific method. Many describe it as an educated assumption, based on prior knowledge and observation.
What is a Hypothesis?
A hypothesis is a suggested solution to an unexplained occurrence that does not conform to current accepted scientific theory. The basic idea of a hypothesis is that there is no predetermined outcome. For a hypothesis to be called a scientific hypothesis, it has to be something that can be supported or refuted by carefully crafted experimentation or observation.
A key function in this step in the scientific method is to derive predictions from hypotheses about the results of future experiments and then perform those experiments to see if they support the predictions. Hypothesis research is made up of three main activities:
Generation of hypotheses: it is the exposition of the hypothesis as such.
Hypothesis evaluation: refers to the evaluation of the relative plausibility of the hypotheses given the available evidence.
Hypothesis testing: That is, the search for more evidence.
Types of Hypotheses
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.
During a test, the scientist may attempt to prove or disprove only the null hypothesis or test both the null and the alternative hypothesis. If a hypothesis specifies a certain direction, it is called a one-tail hypothesis. This means that the scientist believes that the result will be effectful or ineffective.
When a hypothesis is created without predicting the outcome, it is called a two-tailed hypothesis because there are two possible outcomes. The result could be effectful or ineffective, but until the test is complete, there is no way to know what result it will be.
During testing, a scientist can find 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.
How to write a hypothesis
- To write the null and alternative hypotheses for an investigation, we must identify the key variables in the study. The investigator manipulates the independent variable and the dependent variable is the outcome that is measured.
- Operationalize the variables being investigated. Operational variables (or operational definitions) refer to how you will define and measure a specific variable as used in the study.
- Decide a direction for prediction. If there is evidence in the literature to support a specific effect on the independent variable on the dependent variable, we should write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, we should write a non-directional (two-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, we should write a non-directional (two-tailed) hypothesis.
- Write the hypothesis. A good hypothesis is short (that is, concise) and includes clear and simple language.
Testing a hypothesis
The main feature of a hypothesis is that something can be tested and that those tests can be replicated. 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 further in the scientific method, as it is difficult to gather enough supporting evidence.
Upon analysis of the results, a hypothesis can be rejected or modified, but it can never be proven correct 100 percent of the time. For example, relativity has been proven many times, so it is generally accepted as true, but there could be an instance, which has not been found, in which it is not true.
The Evolution of Hypothesis Research
Most formal hypotheses consist of concepts that can be connected and test their relationships. A group of hypotheses come together to form a conceptual framework. As enough data and evidence is collected to support a hypothesis, it becomes a working hypothesis, which is a milestone on the road to becoming a theory. Although hypotheses and theories are often confused, theories are the result of a proven hypothesis. While hypotheses are ideas, theories explain the test results of those ideas.
A related, though subtly different, notion is that of hypothesis-driven research, in which a single hypothesis is selected relatively early in the process, and most of the effort is devoted to substantiating this hypothesis. It is hypothesis-based research with all the attention focused on one conjecture, at least as long as you are not obliged to reject it and consider another.
Pitfalls in Hypothesis Research
Hypothesis research fails, in its simplest form, when we take the wrong hypothesis as true. This can have dire consequences if costly actions are taken. Hypothesis research also fails when there is wrong or excessive reliance on a hypothesis, even if it turns out to be correct. It causes no conclusion to be reached, when more careful research might have revealed that a hypothesis was more plausible. There are three main pitfalls that lead to these failures.
That is, without considering the full range of reasonable hypotheses. A lot of effort is put into investigating one or a few hypotheses, usually obvious, while other possibilities are not considered at all. Too often, one of those others is, in fact, the right one.
Abusing the evidence
Here the evidence already available is not properly evaluated, leading to erroneous assessments of the plausibility of hypotheses. A particular piece of evidence could be considered stronger or more significant than it actually is, especially if it seems to support your preferred hypothesis. By contrast, “negative” evidence, one that directly undermines your preferred hypothesis, or seems to strongly support another, is considered weak or worthless. In addition, the entire body of evidence that is based on a hypothesis could be misqualified. Some pieces of lousy evidence could collectively be considered equivalent to a solid case.
Search in the wrong places
When you search for additional evidence, you instinctively look for information that is actually useless or at least not very helpful in terms of helping you determine the truth. In particular, we are prone to “confirmation bias”, which seeks information that gives weight to our preferred hypothesis. We tend to think that by accumulating a large amount of supporting evidence, we are rigorously testing the hypothesis. But this is a classic mistake. In particular, we are prone to “confirmation bias,” which seeks information that gives weight to our preferred hypothesis. We tend to think that by accumulating a lot of supporting evidence, we are rigorously testing the hypothesis.
But this is a classic mistake. We need to know not only that there is a lot of evidence consistent with our preferred hypothesis, but also that there is inconsistent evidence with the alternatives. You should look for the right kind of evidence relative to your entire set of hypotheses, rather than just a lot of evidence consistent with one hypothesis. This can have two unfortunate consequences. The search can be
Ineffective: No evidence is ever found that could have strongly ruled out one or more “inside” or “outside” hypotheses.
Inefficient: The hypothesis testing process can take much more time and resources than it really should.
General guidelines for investigating good hypotheses
Investigate a wide range of hypotheses
Our natural tendency is to cling to the first plausible hypothesis we can think of and start shaking it hard. This must be resisted. From the outset, you should examine as wide a range of hypotheses as reasonably possible. It is impossible to analyze all hypotheses and it is absurd to even try. But a wide selection of hypotheses can and should be taken into account, including at least some “distant shots”. In generating this set of hypotheses, diversity is at least as important as quantity. We should continue to look for additional hypotheses throughout the research. Incoming information can suggest interesting new possibilities.
Investigate multiple hypotheses
At any time we must keep a series of hypotheses in play. In hypothesis testing, that is, in the search for new information, we must look for information that discriminates what will be revealing in relation to multiple hypotheses at once.
Look for evidence that does not confirm
Instead of trying to prove that some hypothesis is correct, we should try to prove it false. Ideally, we should try to deconfirm multiple hypotheses at the same time. This may be easier if the set of hypotheses is organized hierarchically, allowing you to look for evidence that we eliminate entire groups of hypotheses at once.
Some methodologies have been developed to help with hypothesis research. Methodologies have some important advantages over proceeding in an intuitive or spontaneous way. They are designed to help us avoid cheating, and they do so by incorporating, to some extent, the general guidelines above. They provide distinctive external representations that help us organize and understand sets of hypotheses and evidence. These external representations reduce the cognitive load involved in keeping a lot of related information in complex ways in our heads.
The hypothesis is what the researchers predict in relation to two or more variables, but it involves more than an assumption. Most of the time, the hypothesis begins with a question that is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Most of the time, the hypothesis begins with a question that is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Unless we are creating a study of an exploratory nature, the hypothesis should always explain what we expect to happen during the course of our experiment or research.
Let’s remember that a hypothesis does not have to be correct. While the hypothesis predicts what researchers expect to see, the goal of the research is to determine whether this assumption is right or wrong. By conducting an experiment, researchers could explore a number of factors to determine which ones might contribute to the end result.
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Kim, J.O. y Mueller, Ch. (1978 a). Introduction to factor analysis. Sage University Paper. Serie: Quantitative Applications in the Social Sciences, no. 13. Beverly Hills y Londres: Sage Publications.
Mora y Araujo, M. (1971a). El análisis de relaciones entre variables, la puesta a prueba de hipótesis sociológica. Buenos Aires: Nueva Visión.
Mora y Araujo, M. (1971b). Medición y construcción de índices. Buenos Aires: Nueva Visión.