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For two independent samples of sizes $m,n$, rank their pooled observations and let $W_X$ be the sum of the ranks of the first sample. With continuous observations, the null hypothesis of a common distribution makes every allocation of $m$ ranks to that sample equally probable. A one-sided alternative that the first sample is stochastically smaller rejects for small $W_X$, equivalently small $U_X$. In a common-shape location model this tests a negative location difference. Without the common-shape assumption it is not simply a test of medians. The shifted statistic $U_X$ counts cross-sample pairs for which the first observation exceeds the second. Ties use average ranks and a conditional permutation test or a tie-adjusted normal approximation.

The two-sample Kolmogorov-Smirnov test compares two empirical distribution functions from independent samples, each internally independent and identically distributed, under the null hypothesis that their underlying probability distributions coincide. Both curves are estimated from data, so calibration depends on both sample sizes; the usual asymptotic scaling is $\sqrt{nm/(n+m)}{D}_{n,m}$. Tied data can be handled by an appropriate permutation test under the required exchangeability, rather than by assuming the untied continuous-null law.^[ref]