The purpose of analysis of variance (ANOVA) is to test for significant differences between means by comparing (i.e., analyzing) variances. More specifically, by partitioning the total variation into different sources (associated with the different effects in the design), we are able to compare the variance due to the between-groups (or treatments) variability with that due to the within-group (treatment) variability. Under the null hypothesis (that there are no mean differences between groups or treatments in the population), the variance estimated from the within-group (treatment) variability should be about the same as the variance estimated from between-groups (treatments) variability.

For more information on ANOVA, see the ANOVA/MANOVA, General Linear Models (GLM) or General Regression Models (GRM) modules. For related methods see also Generalized Linear/Nonlinear Models (GLZ) or Partial Least Squares (PLS).