Tukey HSD after ANOVA

[Dragan]

So interesting. But what if both dependent and independent variables have distributions other than normal, but similar to each other? I think (if I understood Dason correctly) in that case, despite the non-normal distribution of dependent variable, we have normal distribution of residual?

You're misinterpreting Dason. See, you can look at ANOVA via Regressioin e.g. Dummy Coded vectors (1's and 0's). When you run the regression and when you perform the tests of normality on the error terms for each group your going to get the same results that you would on the dependent variable Y for each group. The reason is that the difference between the actual Y scores and the errors is simply the mean of groups.

[GretaGarbo]

If Y given x, or the residuals, are not normally distributed you can try transformations. If that does not work you can try for example a generalized linear model with gamma distribution (of Y| x).

But before you do that I suggest Flowerpower (interesting name!) tell us something about the study.

What is “HRV”? (Abbreviations!#3@*#)
Reaction time of what?
How many observations do you have in each cell (i.e. combination)? Is it the same so that the design is balanced?
Is the interaction significant or not?
Are the main effects significant?

I suggest Flowerpower pick out the females and show us box plots for each level of “HRV”.

[victorxstc]

You're misinterpreting Dason. See, you can look at ANOVA via Regressioin e.g. Dummy Coded vectors (1's and 0's). When you run the regression and when you perform the tests of normality on the error terms for each group your going to get the same results that you would on the dependent variable Y for each group. The reason is that the difference between the actual Y scores and the errors is simply the mean of groups.

Thanks Dragan it is interesting. So to make sure I have correctly understood, I might summarize that the distribution of the dependent variable must be normal in order to get normally distributed residuals.