In comparing multiple treatments, 2 error rates that have been studied extensively are the familywise and false discovery rates. Different methods are used to control each of these rates. Yet, it is rare to find studies that compare the same methods on both of these rates, and also on the per-family error rate, the expected number of false rejections. Although the per-family error rate and the familywise error rate are similar in most applications when the latter is controlled at a conventional low level (e.g., .05), the 2 measures can diverge considerably with methods that control the false discovery rate at that same level. Furthermore, we shall consider both rejections of true hypotheses (Type I errors) and rejections of false hypotheses where the observed outcomes are in the incorrect direction (Type III errors). We point out that power estimates based on the number of correct rejections do not consider the pattern of those rejections, which is important in interpreting the total outcome. The present study introduces measures of interpretability based on the pattern of separation of treatments into nonoverlapping sets and compares methods on these measures. In general, range-based (configural) methods are more likely to obtain interpretable patterns based on treatment separation than individual p-value-based measures. Recommendations for practice based on these results are given in the article. Although the article is complex, these recommendations can be understood without the necessity for detailed perusal of the supporting material.