Assumptions in Nonparametric Tests
Jai Prakash V Verma, Abdel‐Salam G. Abdel‐Salam · 2019
This chapter helps readers to understand the required nonparametric assumptions, different nonparametric tests, how to perform those using IBM Statistical Package for the Social Sciences (SPSS) software, and what should be done if there is any violation for these assumptions. The common assumptions in nonparametric tests are randomness and independence. The chi-square test is one of the nonparametric tests for testing three types of statistical tests: the goodness of fit, independence, and homogeneity. In nonparametric analysis, the Mann-Whitney U test is used for comparing two groups of cases on one variable. The Kruskal-Wallis test is considered as an alternative test to the parametric one-way analysis of variance (ANOVA) for comparing more than two groups on one variable. The Wilcoxon Signed-Rank test is an alternative test to the parametric "Paired-samples T-Test" to test the statistical differences in the mean between two related/dependent random samples.