1. Parametric Tests 1) Assumption of population normality 2) homogeneity of variance Parametric more powerful than nonparametric

2. Nonparametric Tests Nonparametric Test (distribution free testing) 1) Pathological conditions are represented by skewed distributions 2) Small clinical samples and samples of convenience cannot be considered representatives of larger nominal distributions 3) Data measured on nominal - category labels ordinal - rank order of observations

3. Non Parametric Parametric Mann-Whitney U TestRank Unpaired T Test

5. Non Parametric Parametric Sign Test Wilcoxon Signed Ranks Test Paired T Test

7. Non Parametric Parametric Kruskal Wallis One-Way Analysis of Variance by Ranks ANOVA F Test

9. Non Parametric Parametric Friedman Two-way Analysis of Variance by Ranks RM ANOVA

11. Chi Square X 2 Non parametric test for analyzing categorical data e.g. yes-no Frequency of occurrence Determines if there is a difference between the proportions observed within a set of categories and the proportions that would be expected by chance.

12. Assumptions 1) frequencies represent individual counts 2) Categories are exhaustive and mutually exclusive