**Assumptions**: These
are the necessary preconditions for fitting a given type of model to data. No
form of generalisation from particular data is possible without assumptions.
They provide the context for, and the means of evaluating, scientific
statements purporting to truly explain reality. As with any statistical test,
ANOVA assumes unbiased sampling from the population of interest. Its other
assumptions concern the error variation against which effects are tested by the
ANOVA model: (i) that the random variation around
fitted values is the same for all sample means of a factor, or across the range
of a covariate; (ii) that the residuals contributing to this variation are free
to vary independently of each other; (iii) that the residual variation
approximates to a normal distribution. Underlying assumptions should be tested
where possible, and otherwise acknowledged as not testable for a given reason
of design or data deficiency.

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/