Herman Rubin wrote: > In article <[EMAIL PROTECTED]>, > dennis roberts <[EMAIL PROTECTED]> wrote: > > >> actually, p values are rather useless (i am almost prepared to say >> "useless") since, it would be the RARE case when the null is REALLY >> exactly true > >> thus, in 99.9999% of the cases ... we KNOW the null is not true so, >> setting some cutoff for rejection and then actually rejecting the >> null ... what has this added to our knowledge? > >> and, p values don't speak to the notion of the null being >> "approximately" true > > I do not see any way to do this other than a decision > theoretic approach
... but it is covered by the use of a composite null hypothesis, which is in all the major theoretical books, even if they are little used practically except for one-sided tests which form a major example. You could have an interval as the null hypothesis. ... and it is also covered by the use of confidence intervals, provided that these are constructed and interpreted via the "test-inversion" approach: thus you can see immediately the parameter values that would or would not be accepted by a significance test as being "true". David Jones . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
