Explanation : Non-parametric tests vs. Parametric tests: Tests appropriate for analyzing ordinal and nominal data are called non-parametric tests. In contrast, tests for analyzing interval or ratio scale are called parametric tests. Parametric tests (z, t or F) require that certain assumption s be valid concerning the population from where the samples were drawn while non-parametric require few assumptions.Tests involving ranks of data are non-parametric. Non-parametric tests are not as powerful as parametric statistics and tend to err on the conservative side. Examples of Non-parametric tests: ∎ Chi-square test: Test of hypothesis to determine if categorical data shows dependency or if two classifications are independent. ∎ One sample sign test: Test of hypothesis related single value for given data. ∎ Two sample sign test, Fisher-Irwin test, Rank sum test (Wilcoxon-Mann-Whitney test i.e., U test, Kruskal-Wallis test i.e., H test), Wilcoxon Matched pairs test/Signed Rank test: Test of hypothesis related no difference among two or more sets of data. ∎ Charles Spearman’s rank correlation, Kendall’s Coefficient of concordance: Test of hypotheses related to relationship between variables. ∎ Kruskal-Wallis test: Test of hypothesis between more than two sets of data are analogous to ANOVA in parametric test.