Hypothesis testing with multiple outcomes requires adjustments to control Type I error inflation, which reduces power to detect significant differences. Maintaining the prechosen Type I error level is challenging when outcomes are correlated. This problem concerns many research areas, including neuropsychological research in which multiple, interrelated assessment measures are common. Standard p value adjustment methods include Bonferroni-, Sidak-, and resampling-class methods. In this report, the authors aimed to develop a multiple hypothesis testing strategy to maximize power while controlling Type I error. The authors conducted a sensitivity analysis, using a neuropsychological dataset, to offer a relative comparison of the methods and a simulation study to compare the robustness of the methods with respect to varying patterns and magnitudes of correlation between outcomes. The results lead them to recommend the Hochberg and Hommel methods (step-up modifications of the Bonferroni method) for mildly correlated outcomes and the step-down minP method (a resampling-based method) for highly correlated outcomes. The authors note caveats regarding the implementation of these methods using available software. Neuropsychological datasets typically consist of multiple, partially overlapping measures, henceforth termed outcomes. A given neuropsychological domain-for example, executive function-is composed of multiple interrelated subfunctions, and frequently all subfunction outcomes of interest are subject to hypothesis testing. At a given α (critical threshold), the risk of incorrectly rejecting a null hypothesis, a Type I error, increases as more hypotheses are tested. This applies to all types of hypotheses, including a set of two-group comparisons across multiple outcomes (e.g., differences between two groups across several cognitive measures) or multiple-group comparisons within an analysis of variance framework (e.g., cognitive performance differences between several treatment groups and a control group). Collectively, we define these issues as the multiplicity problem (Pocock, 1997).Controlling Type I error at a desired level is a statistical challenge, further complicated by the correlated outcomes prevalent in neuropsychological data. By making adjustments to control Type I error, we increase the risk of incorrectly accepting a null hypothesis, a Type II error. In other words, we reduce power. Failure to control Type I error when examining multiple outcomes may yield false inferences, which may slow or sidetrack research progress. Researchers need strategies that maximize power while ensuring an acceptable Type I error rate.Many methods exist to manage the multiplicity problem. Several methods are based on the Bonferroni and Sidak inequalities (Sidak, 1967;Simes, 1986). These methods adjust α values or p values using simple functions of the number of tested hypotheses (Sankoh, Huque, & Dubey, 1997;Westfall & Young, 1993). Holm (1979), Hochberg (1988), and Hommel (1988 developed Bonferroni derivatives in...