Related Experiment Video
Updated: Jun 2, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
A note on consistency of non-parametric rank tests and related rank transformations
1Carleton University, BC, Canada. dwzimm@telus.net
Abstract:
The extent to which rank transformations result in the same statistical decisions as their non-parametric counterparts is investigated. Simulations are presented using the Wilcoxon-Mann-Whitney test, the Wilcoxon signed-rank test and the Kruskal-Wallis test, together with the rank transformations and t and F tests corresponding to each of those non-parametric methods. In addition to Type I errors and power over all simulations, the study examines the consistency of the outcomes of the two methods on each individual sample. The results show how acceptance or rejection of the null hypothesis and differences in p-values of the test statistics depend in a regular and predictable way on sample size, significance level, and differences between means, for normal and various non-normal distributions.
Related Concept Videos
Ranks
Introduction to Nonparametric Statistics
One of...
Friedman Two-way Analysis of Variance by Ranks
Wilcoxon Rank-Sum Test
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Spearman's Rank Correlation Test
Spearman's test calculates correlation by...
