I am a Phd Student in experimental psychology and I try hard to improve my skills and knowledge about how to analyze my data.
Until my 5th year in Psychology, I thought that the regression-like models (e.g., ANOVA) assume the following things:
- normality of the data
- variance homogeneity for the data and so on
My undergraduate courses lead me to believe that the assumptions were about the data. However in my 5th year, some of my instructors underlined the fact that the assumptions are about the error (estimated by the residuals) and not the raw data.
Recently I was talking about the assumptions question with some of my colleagues who also admited that they discovered the importance of checking the assumptions on the residual only in their last years of university.
If I understand well, the regression-like models make assumptions on the error. Thus it makes sense to check the assumptions on the residuals. If so, why some people check the assumptions on the raw data? Is it because such checking procedure approximate what we would obtain by checking the residual?
I would be very intersting in a disccussion about this issue with some people who have more accurate knowledge than my colleagues and I. I thank you in advance for your answers.