The Nonparametric Statistics course introduces statistical inference methods that rely minimally on distributional assumptions. Topics include foundations of nonparametric methods; tests for dichotomous data and contingency tables; one-sample and paired-sample procedures; two-sample procedures for location, dispersion and distributional differences; one-way and two-way layout problems; tests of independence and rank correlation; nonparametric regression and selected modern extensions such as smoothing, density estimation and censored data analysis. The course emphasizes method selection, data-based practice and interpretation of results in applied research contexts.