On 25/05/07, Frank E Harrell Jr <[EMAIL PROTECTED]> wrote: > [EMAIL PROTECTED] wrote: > > Hi all, > > > > apologies for seeking advice on a general stats question. I ve run > > normality tests using 8 different methods: > > - Lilliefors > > - Shapiro-Wilk > > - Robust Jarque Bera > > - Jarque Bera > > - Anderson-Darling > > - Pearson chi-square > > - Cramer-von Mises > > - Shapiro-Francia > > > > All show that the null hypothesis that the data come from a normal > > distro cannot be rejected. Great. However, I don't think it looks nice > > to report the values of 8 different tests on a report. One note is > > that my sample size is really tiny (less than 20 independent cases). > > Without wanting to start a flame war, are there any advices of which > > one/ones would be more appropriate and should be reported (along with > > a Q-Q plot). Thank you. > > > > Regards, > > > > Wow - I have so many concerns with that approach that it's hard to know > where to begin. But first of all, why care about normality? Why not > use distribution-free methods? > > You should examine the power of the tests for n=20. You'll probably > find it's not good enough to reach a reliable conclusion.
And wouldn't it be even worse if I used non-parametric tests? > > Frank > > > -- > Frank E Harrell Jr Professor and Chair School of Medicine > Department of Biostatistics Vanderbilt University > -- yianni ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
