Having your cake and eating it too: Flexibility and power with mass univariate statistics for ERP data.

ABSTRACT: ERP studies produce large spatiotemporal data sets. These rich data sets are key to enabling us to understand cognitive and neural processes. However, they also present a massive multiple comparisons problem, potentially leading to a large number of studies with false positive effects (a high Type I error rate). Standard approaches to ERP statistical analysis, which average over time windows and regions of interest, do not always control for Type I error, and their inflexibility can lead to low power to detect true effects. Mass univariate approaches offer an alternative analytic method. However, they have thus far been viewed as appropriate primarily for exploratory statistical analysis and only applicable to simple designs. Here, we present new simulation studies showing that permutation

SUBMITTER: Fields EC 

PROVIDER: S-EPMC7269415 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature