Nonparametric Statistics Course
Master nonparametric statistics for messy, skewed, and zero-heavy data. Learn robust tests, effect sizes, bootstrapping, and reproducible R/Python workflows to deliver reliable results and clear reports in demanding real-world studies.

4 to 360 hours of flexible workload
valid certificate in your country
What Will I Learn?
Master modern nonparametric methods for skewed, zero-heavy, and censored clinical outcomes in this focused, hands-on course. Learn robust estimators, rank-based tests, quantile and robust regression, cluster-aware approaches, and practical effect sizes. You will practice EDA, sensitivity checks, bootstrapping, permutation tests, and reproducible reporting tailored to real-world clinical data challenges.
Elevify Differentials
Develop Skills
- Apply robust nonparametric tests: Wilcoxon, Kruskal–Wallis, Dunn, and more.
- Design bootstrap and permutation analyses for skewed, zero-heavy clinical data.
- Compute robust effect sizes: Hodges–Lehmann, Cliff’s delta, rank-biserial r.
- Build quantile and rank-based regression models with categorical covariates.
- Produce reproducible R/Python reports with robust tables, plots, and code.
Suggested Summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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