The Wilcoxon signed-rank test is a non-parametric rank test for statistical hypothesis testing used either to test the location of a population based on a sample of data, or to compare the locations of two populations using two matched samples. [1]
A simple explanation of how to perform the Wilcoxon signed rank test, along with a step-by-step example.
Learn how to read and report Wilcoxon Signed Rank Test results, from p-values and z-scores to effect size and handling ties in your data.
The Wilcoxon Signed Rank Test is a non-parametric statistical test used to compare two related groups. It is often applied when the assumptions for the paired t-test (such as normality) are not met.
The Wilcoxon signed rank test offers a robust alternative to the dependent samples t-test when the data do not meet its assumptions. It allows researchers to analyze changes in ordinal data or non-normally distributed metric data from repeated measures or paired observations.
Learn how the Wilcoxon signed-rank test compares paired observations without assuming normality, when to use it, and how to interpret results.
The Wilcoxon signed rank test (also called the Wilcoxon signed rank sum test) is a non-parametric test to compare data. When the word “non-parametric” is used in stats, it doesn’t quite mean that you know nothing about the population.
20.2 - The Wilcoxon Signed Rank Test for a Median Developed in 1945 by the statistician Frank Wilcoxon, the signed rank test was one of the first "nonparametric" procedures developed.
20.2 - The Wilcoxon Signed Rank Test for a Median | STAT 415
Comprehensive reference guide for Wilcoxon Signed Rank test (non-parametric alternative to paired t-test).