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- From: jerry@ginger.hnrc.tufts.edu (Jerry Dallal)
- Newsgroups: sci.math.stat
- Subject: Re: Testing for Normality
- Message-ID: <1992Sep6.110952.349@ginger.hnrc.tufts.edu>
- Date: 6 Sep 92 16:09:52 GMT
- References: <1992Sep5.064647.15570@constellation.ecn.uoknor.edu> <thompson.715720959@hermes.socsci.umn.edu>
- Organization: USDA HNRC at Tufts University
- Lines: 27
-
- In article <thompson.715720959@hermes.socsci.umn.edu>, thompson@atlas.socsci.umn.edu (T. Scott Thompson) writes:
- > bateman@nsslsun.nssl.uoknor.edu (Monte Bateman) writes:
- >
- >>I would like to test data for normality. The books I have
- >>access to talk about graphing frequencies on "probability paper".
- >
- > Look up the Kolmogorov-Smirnov (sp?) statistic in a more advanced
- > statistics text than the ones you have now.
-
- A clarification: Using the K-S statistic to test for normality,
- calculated after plugging in the sample mean and variance in place of the
- population parameters, is known as
- Lilliefors' [Strunk and White be damned!] test.
-
- A Shapiro-Wilk test or a D'Agostino test would probably serve you better.
- Lilliefors' test tends to be more sensitive to departures from normality
- in the center of the distribution. (One look at the test statistic will
- reveal why.) THe other tests tend to be more sensitive to departures
- from normality in the tails, which is where non-normality is most often a
- problem.
-
- This various tests have been compared most recently in, of all places,
- the Stata Technical Bulletin. I dislike mentioning articles in a computer
- program's documentation/house organ because they are typically neither peer
- reviewed nor widely circulated, but it's there, so what can I say?
- (I have heard rumors that Royston is preparing his contributions for formal
- publication, but they are only second hand rumors.)
-