Nonparametric Statistics Course
This course equips you with nonparametric statistics skills for challenging skewed and zero-heavy datasets. Delve into robust testing, effect measures, bootstrap methods, and R/Python reporting pipelines to produce dependable analyses and professional outputs for rigorous studies. Master tools that ensure reliable insights from complex real-world clinical data.

from 4 to 360h flexible workload
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. Explore robust estimators, rank tests, quantile regression, cluster methods, and effect sizes. Apply EDA, bootstrapping, permutation tests, and reproducible workflows suited to clinical data realities.
Elevify advantages
Develop skills
- Apply key nonparametric tests like Wilcoxon, Kruskal-Wallis, and Dunn procedures.
- Conduct bootstrap and permutation tests for skewed clinical datasets.
- Calculate robust effect sizes including Hodges-Lehmann and Cliff’s delta.
- Develop quantile and rank regression models using categorical factors.
- Generate reproducible reports in R or Python with tables, visuals, 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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