Which Wilcoxon should we use? An interactive rank test and other alternatives

Classical nonparametric tests to compare multiple samples, such as the Wilcoxon test, are often based on the ranks of observations. We design an interactive rank test called i-Wilcoxon -- an analyst is allowed to adaptively guide the algorithm using observed outcomes, covariates, working models and prior knowledge -- that guarantees type-I error control using martingales. Numerical experiments demonstrate the advantage of (an automated version of) our algorithm under heterogeneous treatment effects. The i-Wilcoxon test is first proposed for two-sample comparison with unpaired data, and then extended to paired data, multi-sample comparison, and sequential settings, thus also extending the Kruskal-Wallis and Friedman tests. As alternatives, we numerically investigate (non-interactive) covariance-adjusted variants of the Wilcoxon test, and provide practical recommendations based on the anticipated population properties of the treatment effects.