This paper reviews and evaluates three approaches to controlling the familywise error rate (FWE) in functional neuroimaging: Bonferroni, random field theory (RFT), and permutation tests. The authors highlight the challenges of multiple testing in functional neuroimaging, where a large number of correlated test statistics must be assessed. They discuss the limitations of Bonferroni and introduce improved Bonferroni procedures like Hochberg's method, which can handle dependent data. RFT methods use the smoothness of the image to adapt to the severity of the multiple testing problem, while permutation and bootstrap methods are also applicable due to increased computing power. The evaluation is conducted using simulations and real datasets, showing that Bonferroni-related tests offer little improvement over Bonferroni, while the permutation method offers substantial improvement over RFT for low smoothness and low degrees of freedom. The paper also discusses the limitations of finding an equivalent number of independent tests for correlated test statistics.This paper reviews and evaluates three approaches to controlling the familywise error rate (FWE) in functional neuroimaging: Bonferroni, random field theory (RFT), and permutation tests. The authors highlight the challenges of multiple testing in functional neuroimaging, where a large number of correlated test statistics must be assessed. They discuss the limitations of Bonferroni and introduce improved Bonferroni procedures like Hochberg's method, which can handle dependent data. RFT methods use the smoothness of the image to adapt to the severity of the multiple testing problem, while permutation and bootstrap methods are also applicable due to increased computing power. The evaluation is conducted using simulations and real datasets, showing that Bonferroni-related tests offer little improvement over Bonferroni, while the permutation method offers substantial improvement over RFT for low smoothness and low degrees of freedom. The paper also discusses the limitations of finding an equivalent number of independent tests for correlated test statistics.