Non-Parametric Hypothesis Tests Menu

Non-Parametric Hypothesis Tests make very general assumptions about the underlying distribution of the population from which the sample is drawn. This makes such tests a good choice when the assumptions of a parametric test are not met. Since they make fewer assumptions, they are more robust than their corresponding parametric tests. On the flip side, non-parametric tests tend to be less powerful than their parametric counterparts when the parametric test assumptions do hold.

IMPORTANT Note:

The non-parametric tests available in EngineRoom require one or more continuous variables in order to compare their medians. You cannot use summary data with non-parametric tests, because these tests are based on ranking items or differences.

The Non-Parametric Hypothesis Test Menu shows the list of non-parametric tests available: