Sure, let's go ahead and match each test of independent groups on the left with its repeated measures analogue on the right.
1) Chi-Square Test: The chi-square test is used for categorical data to assess the relationship between two variables. It's analogous in repeated measures, is McNemar's Test. McNemar's test is a statistical method used to determine whether the row and column marginal frequencies in a 2 x 2 contingency table are equal (often used in case-control studies).
2) Mann-Whitney U test: This test is a nonparametric test that allows two groups of data to be compared without making the assumption of normally distributed data. The Wilcoxon Signed Rank Test serves a similar function in the case of repeated measures. This test compares two related samples, matched samples, or repeated measurements on a single sample to assess whether their population mean ranks differ.
3) Kruskal-Wallis Test: This is a nonparametric test that can be used to compare three or more groups of data. It is the independent groups analogue of the Friedman Test. The Friedman test is a non-parametric statistical test developed to compare the differences between different treatments in a randomized block design repeated measures study.
4) Independent Samples t-test: This is used to compare the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. The repeated measures equivalent of this test is the Paired Samples t-test. The Paired Samples t-test is a parametric test that is used to compare two population means in the case of two samples that are correlated.
So, we get the following pairs:
1) chi-square test - McNemar's Test,
2) mann-whitney u test - Wilcoxon Signed Rank Test,
3) kruskal-wallis test - Friedman Test,
4) independent samples t-test - Paired Samples t-test.