A few months ago I posted a question about homoscedasticity tests in R on SO, and Ian Fellows answered that (I'll paraphrase his answer very loosely):
Homoscedasticity tests are not a good tool when testing the goodness of fit of your model. With small samples, you don't have enough power to detect departures from homoscedasticity, while with big samples you have "plenty of power", so you are more likely to screen even trivial departures from equality.
His great answer came as a slap in my face. I used to check normality and homoscedasticity assumptions each time I ran ANOVA.
What is, in your opinion, best practice when checking ANOVA assumptions?