Nonparametric Statistics Course
Gain expertise in nonparametric statistics for handling skewed, zero-heavy, and censored clinical data. This course teaches robust tests, effect sizes, bootstrapping, permutation methods, and reproducible R/Python workflows to produce reliable analyses and clear reports for real-world studies. Master exploratory data analysis, sensitivity checks, quantile regression, and cluster-aware approaches tailored to clinical challenges.

from 4 to 360h flexible workload
certificate valid in your country
What will I learn?
Master nonparametric methods for skewed, zero-heavy, and censored clinical data through hands-on practice. Learn robust estimators, rank-based tests, quantile and robust regression, cluster-aware techniques, and practical effect sizes. Apply EDA, sensitivity analysis, bootstrapping, permutation tests, and reproducible reporting for real-world clinical data.
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 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 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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