[R] normality tests [Broadcast]

[gatemaze at gmail.com]

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...

Thanks again.

On 25/05/07, Lucke, Joseph F <Joseph.F.Lucke at uth.tmc.edu> 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: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Liaw, Andy
> Sent: Friday, May 25, 2007 12:04 PM
> To: gatemaze at gmail.com; Frank E Harrell Jr
> Cc: r-help
> Subject: Re: [R] normality tests [Broadcast]
>
> From: gatemaze at gmail.com
> >
> > On 25/05/07, Frank E Harrell Jr <f.harrell at vanderbilt.edu> wrote:
> > > gatemaze at gmail.com 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
> >
> > ______________________________________________
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> > PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
> >
> >
>
>
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-- 
yianni