GOUAL, Hafida2025-11-052025-11-052025https://dspace.univ-annaba.dz//handle/123456789/4291This handout course on nonparametric statistics provides an accessible introduction to fundamental concepts and methods that do not rely on strict assumptions about data distribution. Students will explore the key differences between parametric and nonparametric inference, gaining insights into when to apply these techniques. The course covers essential topics such as ranks, medians, and order statistics, alongside practical applications of statistical tests like the Mann-Whitney and Kruskal-Wallis tests. Participants will learn to construct nonparametric estimators for cumulative distribution functions and probability density functions, using tools like histograms and kernel density estimation. The course also introduces resampling methods, including the Jackknife and Bootstrap, enabling students to estimate standard errors and build confidence intervals. Through hands-on exercises and examples, students will enhance their ability to perform and interpret nonparametric hypothesis tests, making informed decisions about their use in various scenarios. By the end of the course, participants will be equipped with the knowledge and skills to effectively implement nonparametric methods in their statistical analyses.PDFenRank-Based Methods; distribution-Free Tests; Mann-Whitney U TestOther