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Jump to navigation Jump to search “Critical region” redirects here. For the computer science notion of a “critical section”, sometimes called a “critical region”, see critical section. A statistical hypothesis, sometimes called confirmatory data analysis, is a hypothesis that is testable on the basis of observing a process that is modeled via a set of random variables. An alternative framework for statistical hypothesis testing is to specify a set of statistical models, one for each candidate hypothesis, and then use model selection techniques to choose the most appropriate model. Confirmatory data analysis can be contrasted with exploratory data analysis, which may not have pre-specified hypotheses.
Statistical hypothesis testing is a key technique of both frequentist inference and Bayesian inference, although the two types of inference have notable differences. One naïve Bayesian approach to hypothesis testing is to base decisions on the posterior probability, but this fails when comparing point and continuous hypotheses. In the statistics literature, statistical hypothesis testing plays a fundamental role.
At a significance level of 0. It is hard to imagine a situation in which a dichotomous accept – these define a rejection region for each hypothesis.
There is an initial research hypothesis of which the truth is unknown. The first step is to state the relevant null and alternative hypotheses. This is important, as mis-stating the hypotheses will muddy the rest of the process. Decide which test is appropriate, and state the relevant test statistic T.