Non-Parametric Tests
Non-parametric tests are statistical methods used for hypothesis testing when data does not meet the assumptions of parametric tests, such as normality or equal variances. They are distribution-free techniques that rely on ranks, signs, or frequencies rather than specific population parameters. Common examples include the Mann-Whitney U test, Kruskal-Wallis test, and Wilcoxon signed-rank test.
Developers should learn non-parametric tests when working with data that is skewed, has outliers, or comes from small sample sizes, as they provide robust alternatives to parametric tests like t-tests or ANOVA. They are essential in fields like data science, machine learning, and A/B testing for analyzing non-normal or ordinal data, ensuring valid statistical inferences without strict distributional assumptions.