title: Statistical testing in electrophysiological studies.
abstract: This article describes the mechanics and rationale of four different approaches to the statistical testing of electrophysiological data: (1) the Neyman-Pearson approach, (2) the permutation-based approach, (3), the bootstrap-based approach, and (4) the Bayesian approach. These approaches are evaluated from the perspective of electrophysiological studies, which involve multivariate (i.e., spatiotemporal) observations in which source-level signals are picked up to a certain extent by all sensors. Besides formal statistical techniques, there are also techniques that do not involve probability calculations but are very useful in dealing with multivariate data (i.e., verification of data-based predictions, cross-validation, and localizers). Moreover, data-based decision making can also be informed by mechanistic evidence that is provided by the structure in the data.
authors.affiliation: Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands. e.maris@donders.ru.nl
authors.forename: Eric
authors.initials: E
authors.lastname: Maris
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D004594:Electrophysiology / Q000706:statistics & numerical data*; D011336:Probability; D015203:Reproducibility of Results