[R] normality tests [Broadcast]

Sat May 26 00:07:18 CEST 2007

You can also try validating your regression model via the bootstrap (the
validate() function in the Design library is very helpful).  To my mind
that would be much more reassuring than normality tests performed on twenty
residuals.

By the way, be careful with the correlation test - it's only good at
detecting linear relationships between two variables (i.e. not helpful for
detecting non-linear relationships).

Regards,
   -Cody

Cody Hamilton, PhD
Edwards Lifesciences

             gatemaze at gmail.co                                             
             m                                                             
             Sent by:                                                   To 
             r-help-bounces at st         "Lucke, Joseph F"                   
             at.math.ethz.ch           <Joseph.F.Lucke at uth.tmc.edu>        
                                                                        cc 
                                       r-help <r-help at stat.math.ethz.ch>   
             05/25/2007 11:23                                      Subject 
             AM                        Re: [R] normality tests [Broadcast] 

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
> >
> > ______________________________________________
> > R-help at stat.math.ethz.ch 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.
> >
> >
> >
>
>
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> ______________________________________________
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>

--
yianni

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