Nonparametric Statistics Course
Dive into nonparametric statistics tailored for messy, skewed, and zero-heavy clinical data. Gain skills in robust tests, effect sizes, bootstrapping, and reproducible R/Python workflows to produce reliable results and clear reports for real-world studies. This hands-on course equips you with practical tools for challenging data scenarios in clinical research.

4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
Master nonparametric methods for skewed, zero-heavy, and censored clinical data. Explore robust estimators, rank-based tests, quantile regression, cluster-aware techniques, and practical effect sizes. Hands-on practice includes exploratory data analysis, sensitivity checks, bootstrapping, permutation tests, and reproducible reporting for real-world clinical challenges.
Elevify advantages
Develop skills
- Apply robust nonparametric tests like Wilcoxon, Kruskal-Wallis, and Dunn.
- Design bootstrap and permutation analyses for skewed, zero-heavy clinical data.
- Compute robust effect sizes including Hodges-Lehmann, Cliff’s delta, and 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 the 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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