Herman Rubin wrote:
>
> In article <[EMAIL PROTECTED]>,
> Alan McLean <[EMAIL PROTECTED]> wrote:
>
> >"Robert J. MacG. Dawson" wrote:
>
> >> > Alan McLean wrote:
> >> The p value is a direct measure of 'strength of evidence'.
>
> >> and Lise DeShea responded:
>
> ...................
>
> >There is certainly no contradiction. A small p value indicates that the
> >effect (whatever its size!) is (probably) valid. (Use the word 'genuine'
> >if you prefer.)
>
> The effect is (probably) valid in any case. What is being
> tested, which is often not what it is said is being tested,
> is almost certainly false.
>
> >The effect may be too small to be of much use, but that is a very
> >different question.
>
> But this should be the only question. What action should
> be taken?
It cannot possibly be the only question.
One of the roles of statistics, and it is performed particularly by
hypothesis testing, is to be conservative - to stop people from taking
foolish actions by jumping to conclusions. If you observe a large
effect, you shout whoopee! and jump in - invest your life savings, write
your world shattering paper, or whatever. Then your friendly
neighbourhood statistician does a test on your data and points out that
this large effect appears to be mostly a matter of chance - it was not
'significant'. He does say that it *might* be genuine! But you are more
likely to get egg on your face.......
Of course the size of the (apparent) effect and its significance are
related. But both are important.
On a different issue, the frequent claim that 'the null is always false'
is a meaningless statement - at best, irrelevant. A significance test
compares two *models*, providing evidence as to which of them is
(probably) the better choice. It does not pretend to say anything about
'true' values of parameters, and does not deal with exactitude.
Unfortunately it is usually taught in those terms - leading to such
ideas as 'the null is always false'!
Regards, to all,
Alan
> --
> This address is for information only. I do not claim that these views
> are those of the Statistics Department or of Purdue University.
> Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
> [EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558
>
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--
Alan McLean ([EMAIL PROTECTED])
Department of Econometrics and Business Statistics
Monash University, Caulfield Campus, Melbourne
Tel: +61 03 9903 2102 Fax: +61 03 9903 2007
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