On 12 Mar 2001 14:25:41 GMT, [EMAIL PROTECTED] (Radford Neal)
wrote:
[ snip, baseball game; etc. ]
> In this context, all that matters is that there is a difference. As
> explained in many previous posts by myself and others, it is NOT
> appropriate in this context to do a significance test, and ignore the
> difference if you can't reject the null hypothesis of no difference in
> the populations from which these people were drawn (whatever one might
> think those populations are).
So far as I remember, you are the only person who imagined that
procedure, "do a test and ignore ... if you can't reject...." Oh,
maybe Jim, too.
I know that I was explicit in saying otherwise. I said something
like, If your data aren't good enough so you can quantify this mean
difference with a t-test, you probably should not be offering means as
evidence. And, Many of us statisticians find tests to be useful,
even when they are not wholly valid. As evidence, I pointed to the
(over-) acceptance of observational studies in epidemiology. I think
I made those arguments at least two or three times, each.
As it turns out, the big gap in the "scores" makes those averages
dubious, even though a t-test *is* nominally significant.
(That's so, when computed on X or on log(X), but not so, on 1/X.)
And then, as I later discovered, the arguments and the
style of the original report make Jim's criticism tenuous.
Even if you were to illustrate how all the males have
out-achieved all the females, by one criterion or by several
criteria, you would not discredit the decision of the dean --
Wasn't the report was talking more about
'what all our faculty deserve' instead of what's earned by
individuals? You guys have skipped that half.
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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