Summary: A procedure is studied that uses rank-transformed data to perform exact and estimated exact tests, which is an alternative to the commonly used \(F\)-ratio test procedure. First, a common parametric test statistic is computed using rank-transformed data, where two methods of ranking – ranks taken for the original observations and ranks taken after aligning the observations – are studied. Significance is then determined using either the exact permutation distribution of the statistic or an estimate of this distribution based on a random sample of all possible permutation.
Simulation studies compare the performance of this method with the normal theory parametric \(F\)-test and the traditional rank transform procedure. Power and nominal type I error rates are compared under conditions when normal theory assumptions are satisfied, as well as when these assumptions are violated. The method is studied for a two-factor factorial arrangement of treatments in a completely randomized design and for a split-unit experiment. The power of the tests rivals the parametric \(F\)-test when normal theory assumptions are satisfied, and is usually superior when normal theory assumptions are not satisfied. Based on the evidence of this study, the exact aligned rank procedure appears to be the overall best choice for performing tests in a general factorial experiment.