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What is Hypothesis Testing Essentially?
Statistical hypothesis testing is a method of making statistical decisions using accurate experimental data. A result is deemed statistically significant if it is unlikely to have had occurred by chance. The decisions are almost always taken using null-hypothesis tests, that is, ones that answer the question:
Assuming the null hypothesis to be true, what is the exact probability of observing a value for the statistical test that is at least as extreme as the “value in question” that was actually observed?
A statistical hypothesis testing includes the five following steps:
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If we trace back the history of science, null hypotheses are always accepted as facts. Some examples of null hypotheses that are usually accepted as being true are:
Any hypothesis is an educated guess about something in the environment around us. It should be testable, by either observation or through experimentation. If we are to propose a hypothesis, it is customary to write a statement. The statement should look something like this:
“If we … (do anything to a certain independent variable)….then (this will happen to the dependent variable).”
For example:
In statistics, p-value is the probability of obtaining results at the very least as extreme as the observed results of the said statistical hypothesis test, assuming the null hypothesis to be correct. The p-value is used as alternative to rejection points to give the smallest level of significance at which the null hypothesis would be rejected. A smaller p-value tends to mean that there is more substantial evidence in favour of the alternative hypothesis.
Bonferroni test is a multiple comparison test used in statistical analysis. It is essentially:
Bayesian hypothesis testing helps answer the following question: Can the results from any test or survey be repeated?
Problems most students might face with their Hypothesis Testing assignments:
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