Nonparametric Statistics Course
This course equips you with nonparametric statistics skills for challenging skewed and zero-heavy datasets common in clinical research. Delve into robust testing methods, effect size calculations, bootstrapping procedures, and streamlined R/Python workflows to produce dependable analyses and professional reports for rigorous studies.

flexible workload from 4 to 360h
valid certificate in your country
What will I learn?
Gain expertise in nonparametric techniques for handling skewed, zero-inflated, and censored data in clinical settings through this practical course. You will explore robust estimators, rank tests, quantile regression, cluster methods, and effect measures, while applying exploratory data analysis, sensitivity analysis, bootstrapping, permutation testing, and reproducible reporting suited to clinical data realities.
Elevify advantages
Develop skills
- Apply reliable nonparametric tests including Wilcoxon, Kruskal-Wallis, and Dunn procedures.
- Develop bootstrap and permutation strategies for skewed and zero-heavy clinical datasets.
- Calculate sturdy effect sizes such as Hodges-Lehmann, Cliff’s delta, and rank-biserial r.
- Construct quantile and rank-based regression models incorporating categorical factors.
- Generate reproducible reports in R/Python featuring robust tables, visuals, and scripts.
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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