This statistical tool analyzes differences between two related samples, assessing whether their population medians differ significantly. For example, it could be used to compare pre- and post-treatment measurements on the same individuals to determine treatment effectiveness. The analysis ranks the absolute differences between paired observations, then sums the ranks of positive and negative differences separately. This approach accounts for the magnitude and direction of changes.
Non-parametric tests like this are valuable when data doesn’t meet the assumptions of normality required for parametric tests like the paired t-test. This expands the applicability of statistical analysis to a wider range of datasets, particularly in fields like medicine, psychology, and social sciences where normally distributed data cannot always be guaranteed. Developed by Frank Wilcoxon, this method offers a robust alternative for comparing paired data.
