Event-related potential (ERP) studies produce large spatiotemporal datasets. These rich datasets are key to the ability of ERP to help us understand cognition and neural processes. However, they can also present a massive multiple comparisons problem, leading to a high Type I error rate. Standard approaches to 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, but have thus far been seen as appropriate only for exploratory analysis and only applicable to simple designs. Here we present new simulation studies showing that permutation-based mass univariate tests can be employed with complex factorial designs. Most importantly, we show that mass univariate approaches provide slightly greater power than traditional spatiotemporal averaging approaches when strong a priori time windows and spatial regions are used, and that power decreases only modestly when more exploratory spatiotemporal parameters are used. We argue that mass univariate approaches are preferable to traditional analysis approaches for most ERP studies.