This chapter focuses on multivariate statistical tests, which are powerful tools for analyzing and interpreting data with multiple outcome variables. Unlike univariate tests that examine one dependent variable at a time, multivariate tests consider the relationships and interactions among multiple dependent variables simultaneously. This chapter begins by introducing Hotelling’s T-squared test and MANOVA, which can be seen as the multivariate counterparts of the t-test and ANOVA, respectively. It also shows how to compute PERMANOVA, a non-parametric alternative to MANOVA. This chapter ends with canonical correlation analysis, a multivariate method for examining the correlation between two sets of variables.