Classical statistical methods such as ANOVA, linear regression rely on certain data distribution assumptions. Whenever they are not met, alternative methods such as nonparametric may be used. Learn about their principle, advantages and limitations.
Nonparametric Tests & Rank-Based Methods
Upon completion of this module, participants will be able to:
- Understand the advantages & limitations of nonparametric tests
- Assess when nonparametric tests should be used, based on the data and study objectives
- Decide which nonparametric test to use given the data structure
- Carry out the nonparametric data analysis
- Interpret and report the results
This module is intended for everyone involved in statistical data analysis (comparison of groups). It is especially intended for scientists who carry out experimentation with few replications.
This module introduces the key ideas behind most commonly used nonparametric tests. It assumed that participants have no previous knowledge of statistics or that they have not used it for a long time.
- Conditions for the Application of Parametric Tests
- Use of Nonparametric Tests
- Differences between Parametric & Nonparametric Tests
- Tests Adapted to the Various Experimental Designs
- Case of a Single Group : Sign Test, Wilcoxon
- Case of 2 Groups: Wilcoxon, Mann-Whitney
- Case of More Than 2 Groups: Kruskal-Wallis
- Case of Randomized Complete Block Designs: Friedman
- Applications & Case Studies
Recommended Duration: 0.5 to 1.0 day