This article reviews the mass univariate analysis approach to event-related potentials (ERPs) and magnetic fields (ERFs), which involves conducting thousands of statistical tests and applying powerful corrections for multiple comparisons. The authors highlight the advantages of this method, particularly in situations where there is little a priori knowledge about effect locations or latencies, and in delineating effect boundaries. They discuss four methods for multiple comparison correction: strong control of the family-wise error rate (FWER) via permutation tests, weak control of FWER via cluster-based permutation tests, false discovery rate (FDR) control, and control of the generalized FWER (GFWER). The article provides a critical evaluation of these methods, including their practical considerations and limitations. It also introduces free MATLAB software for implementing these analyses. The authors illustrate the mass univariate approach using a visual oddball paradigm and demonstrate how it outperforms conventional analyses in detecting both expected and unexpected ERP effects. However, they note that mass univariate analyses come at the cost of reduced statistical power compared to a priori tests.This article reviews the mass univariate analysis approach to event-related potentials (ERPs) and magnetic fields (ERFs), which involves conducting thousands of statistical tests and applying powerful corrections for multiple comparisons. The authors highlight the advantages of this method, particularly in situations where there is little a priori knowledge about effect locations or latencies, and in delineating effect boundaries. They discuss four methods for multiple comparison correction: strong control of the family-wise error rate (FWER) via permutation tests, weak control of FWER via cluster-based permutation tests, false discovery rate (FDR) control, and control of the generalized FWER (GFWER). The article provides a critical evaluation of these methods, including their practical considerations and limitations. It also introduces free MATLAB software for implementing these analyses. The authors illustrate the mass univariate approach using a visual oddball paradigm and demonstrate how it outperforms conventional analyses in detecting both expected and unexpected ERP effects. However, they note that mass univariate analyses come at the cost of reduced statistical power compared to a priori tests.