**Re: Multiple samples of different size and not normally distributed: how to compare/s**

Oh, no! Take it easy now! Don’t throw out the child with the bathwater.

Thank you for you help.

I'm still trying to understand deeply the meaning of parameter estimations.

I'm still trying to understand deeply the meaning of parameter estimations.

If you understand how to interpret parameter estimates and the effect of factors it is time to continue.

However, can I somehow imply the "order" of factors? I.e. Can I assert what factor(s) or factor interaction(s) are most relevant?

The practical question would be: I'm using a given combination of A, B and C and I want to improve my performance. I can change only one factor level. Which one should I change to maximize the performance improvement?

The practical question would be: I'm using a given combination of A, B and C and I want to improve my performance. I can change only one factor level. Which one should I change to maximize the performance improvement?

1) anova assumes the normality of residuals. SPSS outputs the results of the Levene's test, which was conducted on Error Variances. Its significance is 0.000, i.e. the hypothesis that variances are equal has to be rejected... does it make Anova results not useful?

2) does anova really work with my boolean factors?

Based on a long reflection, the entire study with ANOVA is completly wrong and inappropriate.

Besides, if you had spent two or three years, that could have been “long reflection”. Now you have spent an afternoon. Cool down!

I need to use Wilcoxon in place of t-test and Kruskal Wallis + Median test + Jonckheere-Terpstra Test. I need to start over, then.

The only good thing of the entire study so far is that I understood ANOVA:

a cold comfort, but if you want things done correctly, you have to figure them out by yourself.

And it is you who need to learn. You who need to figure it out. Although I have helped you very much.

Remenber, maybe you can’t put all your data in the same basket. That can make the distribution look strange.

Now, how does the histogram for the residuals look like, untransformed and after taking the square root transformation and the log transformation?