The Mood median test is a nonparametric k-sample test that determines whether two or more independent groups share the same population median. It splits all observations at the grand median and uses a chi-square test on the resulting contingency table of counts above and at-or-below that median.

Use the Mood median test when you have two or more independent groups, your data are at least ordinal, and you cannot assume normality. It is particularly appropriate when data contain extreme outliers that would distort rank-based tests such as Kruskal-Wallis, because the test depends only on whether each value is above or below the grand median.

A p-value below your significance threshold (e.g., 0.05) indicates statistically significant differences in medians across groups. The contingency table shows how each group's observations distribute relative to the grand median; a group with a large proportion above the grand median likely has a higher underlying median.