Nonparametric Statistics Course
This course equips you with nonparametric stats skills for tricky skewed and zero-heavy datasets. Dive into strong tests, effect sizes, bootstrap methods, and reliable R/Python reporting to produce solid findings and straightforward reports for tough real-life research demands. Perfect for clinical pros facing data messiness.

flexible workload of 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 EDA, bootstrapping, permutation tests, and reproducible workflows suited to everyday clinical data issues.
Elevify advantages
Develop skills
- Apply strong nonparametric tests like Wilcoxon, Kruskal-Wallis, and Dunn.
- Set up bootstrap and permutation tests for skewed clinical data with lots of zeros.
- Calculate reliable effect sizes such as Hodges-Lehmann, Cliff’s delta, and rank-biserial r.
- Develop quantile and rank regression models including category variables.
- Create reproducible R/Python reports featuring robust tables, graphs, and scripts.
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 workloadWhat our students say
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