[EMAIL PROTECTED] wrote: > Thank you all for your replies.... they have been more useful... well > in my case I have chosen to do some parametric tests (more precisely > correlation and linear regressions among some variables)... so it > would be nice if I had an extra bit of support on my decisions... If I > understood well from all your replies... I shouldn't pay soooo much > attntion on the normality tests, so it wouldn't matter which one/ones > I use to report... but rather focus on issues such as the power of the > test...
If doing regression I assume your normality tests were on residuals rather than raw data. Frank > > Thanks again. > > On 25/05/07, Lucke, Joseph F <[EMAIL PROTECTED]> wrote: >> Most standard tests, such as t-tests and ANOVA, are fairly resistant to >> non-normalilty for significance testing. It's the sample means that have >> to be normal, not the data. The CLT kicks in fairly quickly. Testing >> for normality prior to choosing a test statistic is generally not a good >> idea. >> >> -----Original Message----- >> From: [EMAIL PROTECTED] >> [mailto:[EMAIL PROTECTED] On Behalf Of Liaw, Andy >> Sent: Friday, May 25, 2007 12:04 PM >> To: [EMAIL PROTECTED]; Frank E Harrell Jr >> Cc: r-help >> Subject: Re: [R] normality tests [Broadcast] >> >> From: [EMAIL PROTECTED] >> > >> > 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? >> >> I believe what Frank meant was that it's probably better to use a >> distribution-free procedure to do the real test of interest (if there is >> one) instead of testing for normality, and then use a test that assumes >> normality. >> >> I guess the question is, what exactly do you want to do with the outcome >> of the normality tests? If those are going to be used as basis for >> deciding which test(s) to do next, then I concur with Frank's >> reservation. >> >> Generally speaking, I do not find goodness-of-fit for distributions very >> useful, mostly for the reason that failure to reject the null is no >> evidence in favor of the null. It's difficult for me to imagine why >> "there's insufficient evidence to show that the data did not come from a >> normal distribution" would be interesting. >> >> Andy >> >> >> > > >> > > 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. >> > >> > >> > >> >> >> ------------------------------------------------------------------------ >> ------ >> Notice: This e-mail message, together with any >> attachments,...{{dropped}} >> >> ______________________________________________ >> [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. >> > > -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University ______________________________________________ [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.
