Nonparametric Statistics Course
This course teaches nonparametric statistics for skewed, zero-heavy, and censored data common in clinical studies. Participants master robust tests like Wilcoxon and Kruskal-Wallis, effect sizes including Hodges-Lehmann and Cliff’s delta, quantile regression, bootstrapping, permutation tests, and reproducible workflows in R/Python. Hands-on practice covers exploratory data analysis, sensitivity checks, and reporting tailored to real-world challenges, ensuring reliable results in demanding research environments.
4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
Gain expertise in nonparametric methods for handling skewed, zero-inflated, and censored clinical data through practical, hands-on training. Key topics include robust estimators, rank-based tests such as Wilcoxon and Kruskal-Wallis, quantile and robust regression, cluster-adjusted techniques, and practical effect sizes like Cliff’s delta. Develop skills in exploratory data analysis, sensitivity analyses, bootstrapping, permutation testing, and creating reproducible reports in R or Python to tackle authentic clinical data issues.
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
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