Nonparametric Statistics Course
This course teaches nonparametric statistics for skewed, zero-heavy, and censored data in clinical studies. Participants master robust tests, effect sizes, bootstrapping, permutation methods, quantile regression, and reproducible workflows in R/Python to analyse real-world data effectively and produce reliable reports.

4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
Gain expertise in nonparametric methods for skewed, zero-heavy, and censored clinical data through practical training. Cover robust estimators, rank-based tests, quantile and robust regression, cluster-aware techniques, and effect sizes. Practice exploratory data analysis, sensitivity analysis, bootstrapping, permutation tests, and reproducible reporting for clinical challenges.
Elevify advantages
Develop skills
- Apply robust nonparametric tests including Wilcoxon, Kruskal-Wallis, and Dunn.
- Design bootstrap and permutation analyses for skewed, zero-heavy clinical data.
- Compute robust effect sizes such as 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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