Different ways that dependent variables can cause multivariate significance are illustrated. It is argued that univariate tests should not be used as follow-up procedures to identify the variables that can contribute to multivariate significance. Methods that can be used to determine the contributions individual variables make to multivariate significance are discussed. Two-group multivariate analysis of variance (MANOVA) with two dependent variables is described; a significant multivariate test under three conditions of univariate significance is considered: (1) with both dependent variables significant; (2) with one dependent variable significant; and (3) with both dependent variables not significant. The relationships among the variables in these conditions yield 11 different situations. The data illustrate: multivariate significance can be caused under a variety of relationships; it is improper to interpret univariate analysis of variance results to find the causes of multivariate significance. The use of discriminant analysis and stepwise discriminant analysis is demonstrated as a valuable tool in considering the contributions of dependent variables to multivariate significance. Three tables illustrate the 11 situations, and Appendix A details the process used to create each situation. (SLD)