Unexpectedly for me (!) I've recently learnt that:
"We have assumed that the error terms, $\epsilon_{ij}$, of the variates in each sample will be independent, that the variances of the error terms of the several samples will be equal, and, finally, that the error terms are distributed normally."
[in R.R.Sokal F.J.Rohlf; Biometry, 3rd ed., 1994: p.406, section 13.4]
Does this mean that we have to operate with errors (i.e. with residuals) for checking the assumptions for statistical linear models (including repeated-measures ANOVAs) and abandon raw data?
The question is close to my previous one.
Best Answer
Model assumption of constancy of variance relates to the error term, not to the raw data! The residuals, in some sense, estimates the error term. So for testing model assumptions, you must use the residuals.