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- From: wvenable@algona.stats.adelaide.edu.au (Bill Venables)
- Newsgroups: sci.math.stat
- Subject: Re: Testing for Normality
- Message-ID: <WVENABLE.92Sep12234508@algona.stats.adelaide.edu.au>
- Date: 12 Sep 92 14:15:08 GMT
- References: <1992Sep5.064647.15570@constellation.ecn.uoknor.edu>
- <Bu5suB.HxM@mentor.cc.purdue.edu> <1992Sep10.124312.4391@cognos.com>
- <11SEP199206534334@amarna.gsfc.nasa.gov>
- Organization: Department of Statistics, University of Adelaide
- Lines: 22
- NNTP-Posting-Host: algona.stats.adelaide.edu.au
- In-reply-to: packer@amarna.gsfc.nasa.gov's message of 11 Sep 92 10:53:00 GMT
-
- >>>>> "Charles" == Charles Packer <packer@amarna.gsfc.nasa.gov> writes:
-
- Charles> Why not use a traditional chi-square test?
-
- I can think of two possible reasons:
-
- 1. It requires an arbitrary partition of the range into panels, *before*
- the sample comes to hand. (In fact it really does not test normality as
- such, but rather that the grouped sample distribution agrees with a
- similarly grouped normal.) Arbitrariness always comes at some cost.
-
- 2. In seeking to get some power against a very wide class of alternatives
- it manages only to achieve low power against any subclass, including the
- subclass of practically important alternatives. In this sense it is not
- well focused enough.
-
- [BTW I would be interested in a Bayesian reaction to this question. It always
- seemed to me that tests of fit could be rather an embarrassment to a Bayesian.]
- --
- ___________________________________________________________________________
- Bill Venables, Dept. of Statistics, | Email: venables@stats.adelaide.edu.au
- Univ. of Adelaide, South Australia. | Tel: +61 8 228 5412 Fax: ...232 5670
-