Nonparametric Statistics Course
This course equips you with essential nonparametric statistics skills for challenging data types like skewed distributions and zero-heavy clinical outcomes. Delve into robust testing methods, effect size calculations, bootstrap and permutation procedures, plus reproducible workflows in R and Python to produce trustworthy analyses and professional reports suited to rigorous research environments. Master these tools to confidently tackle real-world data complexities and enhance your statistical reporting capabilities.

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. 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 for authentic clinical datasets.
Elevify advantages
Develop skills
- Apply key nonparametric tests including Wilcoxon, Kruskal-Wallis, and Dunn procedures.
- Develop bootstrap and permutation strategies tailored for skewed and zero-heavy clinical datasets.
- Calculate reliable effect sizes such as Hodges-Lehmann, Cliff’s delta, and rank-biserial r.
- Construct quantile and rank-based regression models incorporating categorical variables.
- Generate reproducible reports in R or Python featuring robust tables, visualizations, and code.
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 workload.What our students say
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