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Module 15 Hypothesis Tests

The goal of this session is to learn how to use parametric, nonparametric, and post hoc hypothesis tests. The properties and characteristics of sample data determine which types of analysis can be appropriately performed on them. Parametric tests are those that make assumptions about the parameters (defining properties) of the population from which their sample data are drawn. Those assumptions include that the underlying source populations have a normal distribution and the sample data are from an interval or ratio scale and are alike with regard to variance. Most well-known elementary statistical methods are parametric. Sometimes, researchers are confronted with experimental situations that do not conform to the requirements of parametric tests. In these situations, it may not be appropriate to use a parametric test. This is particularly important because when the assumptions of a test are violated, the test may lead to an erroneous interpretation of the data. Most non-parametric tests do not state hypotheses in terms of a specific parameter, they make few (if any) assumptions about the population distribution and are usually performed on nominal or ordinal data.