Summary

In this chapter, we discussed some of the most commonly used non-parametric hypothesis tests performed when required assumptions for parametric hypothesis testing cannot be prudently guaranteed. We discussed two-sample Wilcoxon Rank-Sum – also called Mann-Whitney U – tests to draw inferences from medians when two-sample t-testing cannot be performed. Next, we walked through the Wilcoxon Sign-Rank test’s paired comparison of medians when a paired t-test comparison of means cannot be performed. After, we looked at the non-parametric chi-square goodness-of-fit test and the chi-square Test of independence for comparing observed frequencies against expected frequencies, both useful for identifying the presence of statistically significant differences in counts of categorical data. Additionally, we discussed the Kruskal-Wallis test, a non-parametric alternative to the analysis of variance (ANOVA). Finally, we discussed Spearman’s correlation coefficient...