[R] normality tests [Broadcast]
Cody_Hamilton at Edwards.com
Cody_Hamilton at Edwards.com
Sat May 26 00:28:26 CEST 2007
Following up on Frank's thought, why is it that parametric tests are so
much more popular than their non-parametric counterparts? As
non-parametric tests require fewer assumptions, why aren't they the
default? The relative efficiency of the Wilcoxon test as compared to the
t-test is 0.955, and yet I still see t-tests in the medical literature all
the time. Granted, the Wilcoxon still requires the assumption of symmetry
(I'm curious as to why the Wilcoxon is often used when asymmetry is
suspected, since the Wilcoxon assumes symmetry), but that's less stringent
than requiring normally distributed data. In a similar vein, one usually
sees the mean and standard deviation reported as summary statistics for a
continuous variable - these are not very informative unless you assume the
variable is normally distributed. However, clinicians often insist that I
included these figures in reports.
Cody Hamilton, PhD
Edwards Lifesciences
Frank E Harrell
Jr
<f.harrell at vander To
bilt.edu> "Lucke, Joseph F"
Sent by: <Joseph.F.Lucke at uth.tmc.edu>
r-help-bounces at st cc
at.math.ethz.ch r-help <r-help at stat.math.ethz.ch>
Subject
Re: [R] normality tests
05/25/2007 02:42 [Broadcast]
PM
Lucke, Joseph F 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.
I beg to differ Joseph. I have had many datasets in which the CLT was
of no use whatsoever, i.e., where bootstrap confidence limits were
asymmetric because the data were so skewed, and where symmetric
normality-based confidence intervals had bad coverage in both tails
(though correct on the average). I see this the opposite way:
nonparametric tests works fine if normality holds.
Note that the CLT helps with type I error but not so much with type II
error.
Frank
>
> -----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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>>
>
>
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> ______________________________________________
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--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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