🚀 go-pugleaf

>>>>> "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