This note revisits Steel's multiple comparison test which uses Wilcoxon statistics in pairwise comparisons of several treatment samples with a common control sample. It derives means, variances and covariances of the Wilcoxon statistics under the conditional randomization distribution, given the tie pattern in the pooled samples. Sample sizes do not have to be equal. Under the randomization distribution asymptotic multivariate normality of these Wilcoxon statistics is established. This widens the scope of normal approximations to conditional tests, assuming independent samples of respective sizes $n_0, n_1,\ldots,n_K$ from any common population or randomized treatment assignment to $N=n_0 + n_1+\ldots+n_K$ experimental subjects. Significance probabilities are obtained using a normal approximation and a single quadrature. In the continuous shift model the simultaneous tests are converted to simultaneous confidence bounds and intervals. This is implemented in the {\sf R} package {\tt kSamples}. Extensions to all pairwise Wilcoxon test comparisons are discussed.

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