Will Hopkins wrote:
>
>
>
> I haven't followed this thread closely, but I would like to state the
> only valid and useful interpretation of the p value that I know. If
> you observe a positive effect, then p/2 is the probability that the
> true value of the effect is negative. Equivalently, 1-p/2 is the
> probability that the true value is positive.
>
> The probability that the null hypothesis is true is exactly 0. The
> probability that it is false is exactly 1.
>
> Estimation is the name of the game. Hypothesis testing belongs in
> another century--the 20th. Unless, that is, you base hypotheses not
> on the null effect but on trivial effects...
>
With respect, Will, this is a very limited view of statistics in general
and hypothesis testing in particular. One of the features of this view
is that you think in terms of 'true values' rather than models. A null
hypothesis is not 'true' - it may or may not be 'valid' in the sense
that using it enables reasonable predictions.
The same comment can be made of any scientific theory. In what sense is
Relativity 'true'? But it enables reasonable predictions.
Estimation is obviously important - but hypothesis testing, properly
considered, is also essential.
Regards,
Alan
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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