Journal
PSYCHOPHYSIOLOGY
Volume 49, Issue 4, Pages 549-565Publisher
WILEY-BLACKWELL
DOI: 10.1111/j.1469-8986.2011.01320.x
Keywords
Statistical test; Neyman-Pearson; Bayesian inference; Permutation test; Bootstrap; Multiple comparisons problem; Neurobiological data; Mechanistic evidence; Electrophysiology; Common pick-up
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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.
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