SEARCH WITHIN CONTENT
VOLUME 56 , ISSUE 4 ( October-December, 2022 ) > List of Articles
Kamal Kishore, Vidushi Jaswal
Keywords : Brunner–Munzel test, Data interpretation, Mann–Whitney test, Nonparametric test, Two unpaired groups, Wilcoxon rank sum test
Citation Information : Kishore K, Jaswal V. Statistics Corner: Wilcoxon–Mann–Whitney Test. J Postgrad Med Edu Res 2022; 56 (4):199-201.
License: CC BY-NC 4.0
Published Online: 31-12-2022
Copyright Statement: Copyright © 2022; The Author(s).
Wilcoxon–Mann–Whitney (WMW) test is a nonparametric counterpart of the t-test for comparing two unpaired groups. Traditional teaching and many books recommend applying WMW when: (1) continuous outcome variables violate assumptions and (2) data are ordinal. Standard recommendations about the applicability of WMW are not correct. Many health researchers also believe that WMW compares medians between groups; the reporting measure, however, is contextual—it depends on factors such as distribution type, sample size, and heteroscedasticity. A researcher comparing outcomes from two groups found that continuous dependent variables (DVs) do not fulfill the normality and homogeneity of variance assumptions. An initial literature search indicates that nonparametric methods are better for analyzing data. There are, however, a few vital questions concerning analyzing data with WMW: • Does the test make any assumptions? • What it compares—median or mean rank? • What is the null and alternate hypothesis? • What to report and how to interpret results?