title: Mass univariate analysis of event-related brain potentials/fields II: Simulation studies.
abstract: Mass univariate analysis is a relatively new approach for the study of ERPs/ERFs. It consists of many statistical tests and one of several powerful corrections for multiple comparisons. Multiple comparison corrections differ in their power and permissiveness. Moreover, some methods are not guaranteed to work or may be overly sensitive to uninteresting deviations from the null hypothesis. Here we report the results of simulations assessing the accuracy, permissiveness, and power of six popular multiple comparison corrections (permutation-based control of the familywise error rate [FWER], weak control of FWER via cluster-based permutation tests, permutation-based control of the generalized FWER, and three false discovery rate control procedures) using realistic ERP data. In addition, we look at the sensitivity of permutation tests to differences in population variance. These results will help researchers apply and interpret these procedures.
authors: Department of Cognitive Science, University of California-San Diego, La Jolla, CA 92093-0515, USA. dgroppe@cogsci.ucsd.edu
authors: David M
authors: DM
authors: Groppe
authors: Thomas P
authors: TP
authors: Urbach
authors: Marta
authors: M
authors: Kutas
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000706:statistics & numerical data*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D015203:Reproducibility of Results