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A few months ago I posted a question about homoscedascity tests in R on SO, and Ian Fellows answered that (I'll paraphrase his answer very loosely) "homoscedascity 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 homoscedascity, 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 homoscedascity assumptions each time I ran ANOVA.

Now you lads hop in:

What is, in your opinion, best practice when checking ANOVA assumptions?

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?

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

A few months ago I posted a question about homoscedascity tests in R on SO, and Ian Fellows answered that (I'll paraphrase his answer very loosely) "homoscedascity 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 homoscedascity, 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 homoscedascity assumptions each time I ran ANOVA. 

Now you lads hop in:

What is, in your opinion, best practice when checking ANOVA assumptions?