1. **Mann-Whitney U Test** - This test compares differences between two independent groups when the dependent variable is either ordinal or continuous but not normally distributed. - It is a non-parametric alternative to the independent samples t-test. - The test ranks all observations from both groups together and compares the sum of ranks between groups. 2. **Wilcoxon Signed-Rank Test** - Used to compare two related samples or repeated measurements on a single sample to assess whether their population mean ranks differ. - It is a non-parametric alternative to the paired t-test. - The test considers the magnitude and direction of differences between paired observations. 3. **Kruskal-Wallis H Test** - Extends the Mann-Whitney U test to more than two independent groups. - It tests whether samples originate from the same distribution. - It ranks all data points together and compares the sum of ranks across groups. 4. **Friedman Test** - Used for comparing more than two related groups. - It is a non-parametric alternative to repeated measures ANOVA. - The test ranks data within each block (subject) and analyzes the ranks across treatments. 5. **Spearman's Rank Correlation** - Measures the strength and direction of association between two ranked variables. - It is a non-parametric alternative to Pearson's correlation. - It calculates correlation based on the ranks of data rather than raw data values. These tests are preferred when assumptions of normality and homogeneity of variances are violated because they rely on ranks or medians rather than means and variances, making them robust to outliers and non-normal distributions.