Variance refers to the average squared distance of scores from the mean. Like standard deviation, variance is a measure of variability that is important in statistics because it indicates how spread out scores are in a distribution.

Analysis of variance (ANOVA) is a statistical technique designed to compare normally distributed, independent datasets by testing whether or not three or more population means are equal to one another. One-way ANOVA compares multiple groups against one independent variable, while factorial (two-way) ANOVA compares multiple groups against two independent variables. One risk of ANOVA is that there is an inflated chance for a type I error; however, the Bonferroni adjustment corrects this inflation.

"Multivariate analysis of variance (MANOVA) is an extension of the univariate analysis of variance (ANOVA). In an ANOVA, we examine for statistical differences on one continuous dependent variable by an independent grouping variable. The MANOVA extends this analysis by taking into account multiple continuous dependent variables, and bundles them together into a weighted linear combination or composite variable. The MANOVA will compare whether or not the newly created combination differs by the different groups, or levels, of the independent variable. In this way, the MANOVA essentially tests whether or not the independent grouping variable simultaneously explains a statistically significant amount of variance in the dependent variable."