title: Many tests of significance: new methods for controlling type I errors.
abstract: There have been many discussions of how Type I errors should be controlled when many hypotheses are tested (e.g., all possible comparisons of means, correlations, proportions, the coefficients in hierarchical models, etc.). By and large, researchers have adopted familywise (FWER) control, though this practice certainly is not universal. Familywise control is intended to deal with the multiplicity issue of computing many tests of significance, yet such control is conservative--that is, less powerful--compared to per test/hypothesis control. The purpose of our article is to introduce the readership, particularly those readers familiar with issues related to controlling Type I errors when many tests of significance are computed, to newer methods that provide protection from the effects of multiple testing, yet are more powerful than familywise controlling methods. Specifically, we introduce a number of procedures that control the k-FWER. These methods--say, 2-FWER instead of 1-FWER (i.e., FWER)--are equivalent to specifying that the probability of 2 or more false rejections is controlled at .05, whereas FWER controls the probability of any (i.e., 1 or more) false rejections at .05. 2-FWER implicitly tolerates 1 false rejection and makes no explicit attempt to control the probability of its occurrence, unlike FWER, which tolerates no false rejections at all. More generally, k-FWER tolerates k - 1 false rejections, but controls the probability of k or more false rejections at α =.05. We demonstrate with two published data sets how more hypotheses can be rejected with k-FWER methods compared to FWER control.
authors: Department of Psychology, University of Manitoba, Winnipeg, Manitoba, Canada. kesel@cc.umanitoba.ca
authors: H J
authors: HJ
authors: Keselman
authors: Charles W
authors: CW
authors: Miller
authors: Burt
authors: B
authors: Holland
mesh_terms: D015233:Models, Statistical; D011584:Psychology / Q000706:statistics & numerical data; D012107:Research Design / Q000706:statistics & numerical data*; D013223:Statistics as Topic / Q000379:methods*