# Mann-Whitney U and Wilcoxon as post hoc tests

## Explain significant main effects from Kruskal-Wallis tests and Friedman's ANOVA

__are used when analyzing__

**Non-parametric statistics****categorical**and

**ordinal outcomes**. These statistics are also used with

**smaller sample sizes**(n < 20) and when the

**assumptions of certain statistical tests are violated**.

The

**test is employed when**

__Mann-Whitney U__**comparing two independent groups on an ordinal outcome**. It is also used when the

**assumptions**of an independent samples or unpaired t-test

**are**

**violated**(normality, homogeneity of variance).

The

**test is used when**

__Wilcoxon__**comparing ordinal outcomes at two different points in time or within-subjects**. It is further used when the

**assumptions**of a repeated measures t-test

**are violated**(independence of observations, normality of difference scores).

A lesser known use for these two non-parametric tests is

**when significant main effects are found for non-parametric**. Much like with a parametric one-way ANOVA or repeated-measures ANOVA,

__Kruskal-Wallis__and__Friedman's ANOVA__tests**if a significant main effect is found using non-parametric statistics, then a post hoc analysis must be undertaken to explain the significant main effect**. Non-parametric statistics do not have Tukey, Scheffe, and Dunnett tests like parametric statistics!

When a significant main effect is found using a Kruskal-Wallis test,

**subsequent Mann-Whitney U tests must be employed in a post hoc fashion**to explain where amongst the independent groups the actual differences exist.

The same holds true for Friedman's ANOVA. If a significant main effect is found,

**then Wilcoxon tests must be used in a post hoc fashion**to explain where the significant changes occur amongst the observations or within-subjects.