Analysis of Variance (ANOVA): An analysis of the relative contributions of explained and unexplained sources of variance in a continuous response variable. Here we use the term 'ANOVA' in its broad sense to include explanatory factors that vary on continuous as well as categorical scales. In its narrower sense, ANOVA refers to balanced designs with categorical factors, while General Linear Models (GLM) encompass also unbalanced designs and covariates. Parametric ANOVA and GLM partition the total variance in the response by measuring sums of squared deviations from modelled values. Significant effects are tested with the F statistic, which assumes random sampling of independent replicates, homogeneous within-sample variances, and a normal distribution of the residual error variation around sample means.

Doncaster, C. P. & Davey, A. J. H. (2007) Analysis of Variance and Covariance: How to Choose and Construct Models for the Life Sciences. Cambridge: Cambridge University Press.

http://www.southampton.ac.uk/~cpd/anovas/datasets/