Ted Harding <[EMAIL PROTECTED]> [Sun, Jul 13, 2008 at 10:59:21PM CEST]:
> On 13-Jul-08 19:53:47, Johannes Huesing wrote:
> > Frank E Harrell Jr <[EMAIL PROTECTED]> [Sun, Jul 13, 2008 at
> > 08:07:37PM CEST]:
> >> (Ted Harding) wrote:
> >>> On 13-Jul-08 13:29:13, Frank E Harrell Jr wrote:
> >>>> [...]
> >>>> A large P-value means nothing more than needing more data. No
> >>>> conclusion is possible.
[...]
> But "absence
> of evidence", in my interpretation (which I believe is right for
> the statistical context of "non-significant P-values"), means that
> we do not know about A: we do not have enough information.
>
What would the p-value have to be like in your opinion to make the
null hypothesis look more likely after the experiment than before?
> The proof is, basically, given in terms of a 2-valued logic where
> every term is either TRUE or FALSE. In the real world we have at
> least a third possible value: UNKNOWN (or, as R would put it, NA).
How would the probabilities that A is NA be affected by the outcome
of an experiment like this? If this probability is affected, how
does this leave the probability that A is T or F unaffected?
Or do you assign the NA status to the data collected?
A high p-value does not always equate that you might as well have
collected nothing but missing values.
Of course I buy into the notion that a point estimate with a measure
of accuracy is much better suited to describe your data; but a
high p-value as a result of a test procedure that can be claimed to
be adequately powered may defensibly be taken as a hint that we
can for now stick with the null hypothesis.
--
Johannes Hüsing There is something fascinating about science.
One gets such wholesale returns of conjecture
mailto:[EMAIL PROTECTED] from such a trifling investment of fact.
http://derwisch.wikidot.com (Mark Twain, "Life on the Mississippi")
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