Most of the multivariate statistical techniques rely on the assumption of multivariate normality. The effects of non-normality on multivariate tests are assumed to be negligible when variance-covariance matrices and sample sizes are equal. Therefore, in practice, investigators do not usually attempt to remove non-normality. In this simulation study, the effects of non-normality on skewed multivariate data in terms of power were examined by manipulating the factors such as distribution, sample size, number of variables, and variance-covariance matrix. The number of replications was set to 500, and sample sizes of 10, 15, and 20 were used, with 2 sets of variables, and 2 variance-covariance matrices. The multivariate Box-Cox transformation was applied to remove non-normality. The power of multivariate analysis of variance (MANOVA) was then calculated after the transformation. The results were compared with the power calculated before the multivariate Box-Cox transformation was applied. In conclusion, even when variance-covariance matrices and sample sizes were equal, small to moderate increases in power were observed. (Author/SLD)