The article reviews mass univariate analysis of event-related brain potentials (ERPs) and magnetic fields (ERFs), a statistical method that allows for the analysis of vast numbers of data points across time and space. This approach, which involves thousands of statistical tests, is particularly useful when a priori knowledge of effect locations or latencies is limited. It complements and, in some cases, replaces traditional ANOVA-based methods. The paper discusses four methods for controlling multiple comparisons: 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 generalized FWER control. The mass univariate approach is illustrated using a visual oddball paradigm, where ERPs to target and standard words are analyzed. Traditional ANOVA methods miss unexpected effects outside predefined time windows, while mass univariate analysis identifies these effects and provides precise timing and spatial localization. For example, it detects a left frontal N2-like effect, a posterior effect, and a slow wave effect outside the P2 and P3 time windows. Permutation tests provide strong FWER control by computing the distribution of the most extreme statistic across all tests, ensuring a 5% chance of making one or more false discoveries. Cluster-based permutation tests provide weak FWER control by grouping significant results into clusters, which is more powerful than strong FWER control but less certain. FDR control limits the expected proportion of false discoveries, making it less conservative than FWER methods. GFWER control allows a small number of false discoveries, increasing analysis power while providing a clearer sense of the number of false discoveries. Each method has its advantages and limitations. Permutation tests are non-parametric and robust but can be computationally intensive. Cluster-based tests are powerful for detecting broad effects but may miss narrow effects. FDR control is less conservative and more powerful but may overestimate the number of true discoveries. GFWER control balances power and accuracy by allowing a small number of false discoveries. The paper concludes that mass univariate analyses are a valuable complement to traditional ERP/ERF analyses, offering greater temporal and spatial detail while addressing the limitations of conventional methods.The article reviews mass univariate analysis of event-related brain potentials (ERPs) and magnetic fields (ERFs), a statistical method that allows for the analysis of vast numbers of data points across time and space. This approach, which involves thousands of statistical tests, is particularly useful when a priori knowledge of effect locations or latencies is limited. It complements and, in some cases, replaces traditional ANOVA-based methods. The paper discusses four methods for controlling multiple comparisons: 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 generalized FWER control. The mass univariate approach is illustrated using a visual oddball paradigm, where ERPs to target and standard words are analyzed. Traditional ANOVA methods miss unexpected effects outside predefined time windows, while mass univariate analysis identifies these effects and provides precise timing and spatial localization. For example, it detects a left frontal N2-like effect, a posterior effect, and a slow wave effect outside the P2 and P3 time windows. Permutation tests provide strong FWER control by computing the distribution of the most extreme statistic across all tests, ensuring a 5% chance of making one or more false discoveries. Cluster-based permutation tests provide weak FWER control by grouping significant results into clusters, which is more powerful than strong FWER control but less certain. FDR control limits the expected proportion of false discoveries, making it less conservative than FWER methods. GFWER control allows a small number of false discoveries, increasing analysis power while providing a clearer sense of the number of false discoveries. Each method has its advantages and limitations. Permutation tests are non-parametric and robust but can be computationally intensive. Cluster-based tests are powerful for detecting broad effects but may miss narrow effects. FDR control is less conservative and more powerful but may overestimate the number of true discoveries. GFWER control balances power and accuracy by allowing a small number of false discoveries. The paper concludes that mass univariate analyses are a valuable complement to traditional ERP/ERF analyses, offering greater temporal and spatial detail while addressing the limitations of conventional methods.