Date: Fri, 22 Oct 2010 05:33:11 -0700
Reply-To: Bruce Weaver <email@example.com>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Bruce Weaver <firstname.lastname@example.org>
Subject: Re: same data,
same test with different procedures =different results USING 19.0
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> Dear List; I just tested a sample distribution using the One Sample
> Kolmogorow-Smirnov Test under Non-Parametric Tests-One sample K-S test and
> get a K-S Z of .933 with a p value of .349. Indicating that my sample is
> consistent with a population that is normally distributed. I then went
> and used the Explore procedure and looked at the results of the K-S test
> and get some very different results. The statistic reported is .070 and
> the p value is .034. I am not sure what is happening here. Does anyone
> have any ideas. Thanks, martin
Hi Martin. Marta has given a very thorough answer (as usual) to your
question. But I'm still wondering why you want to test for normality.
If you are doing it as a precursor to a t-test (or some other parametric
procedure), I would advise you to not bother. Sampling from (approximately)
normal populations is most important when sample sizes are small. It
becomes less and less important as the sample size increases, because the
sampling distribution converges on the normal. Now consider the test of
normality. When sample sizes are small (and normality is most important for
the t-test), the test of normality has very low power, and will fail to
detect important departures from normality. But when sample sizes are large
(and normality is not so important for the t-test), the test of normality
has too much power--i.e., it will throw up the red flag of non-normality for
unimportant departures from normality. So testing for normality as a
precursor to a t-test or ANOVA is just about the most pointless statistical
exercise one can engage in. IMO, at least. ;-)
"When all else fails, RTFM."
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