In article <[EMAIL PROTECTED]>, David Jones <[EMAIL PROTECTED]> wrote:
>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.

This is likely to be a poor procedure.  The p-value depends on
the precise parameter, and thus is not constant over an interval.
The maximum is not likely to be what is wanted.  The true problem
is still a decision problem.


>... 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".

Confidence intervals, and I include Bayesian intervals, are 
also not justified as decision procedures.  A confidence 
interval can be a descriptive statistic, but even the 
Bayesian ones do not provide the information.  In all cases,
the likelihood function does provide all the information 
in the data.




-- 
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, Deptartment of Statistics, Purdue University
[EMAIL PROTECTED]         Phone: (765)494-6054   FAX: (765)494-0558
.
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