The more commonly known statistical procedures, such as the t-test, analysis of variance, or chi-squared test, can handle only one dependent variable (DV) at a time. Two types of problems can arise when there is more than one DV: 1. a greater probability of erroneously concluding that there is a significant difference between the groups when in fact there is none (a Type I error); and 2. failure to detect differences between the groups in terms of the patterns of DVs (a Type II error). Multivariate statistics are designed to overcome both of these problems. However, there are costs associated with these benefits, such as increased complexity, decreased power, multiple ways of answering the same question, and ambiguity in the allocation of shared variance. This is the first of a series of articles on multivariate statistical tests which will address these issues and explain their possible uses.