On Wed, 1 Feb 2023 23:05:48 -0800 (PST), Cosine <
ase...@gmail.com>
wrote:
> Hi:
>
> Found that many academic journals would require the submission
> report the statistical significance (in terms of p-value or
> confidence interval) of the results; however, it seems less often
> that a journal requires reporting the statistical power of the
> results. Why is that?
If you found something, obviously you had enough power.
In the US, the granting agencies of NIH want to hear what
you have to say about power, to justify giving you money.
I remember a few things relevant about power and journals.
1970s - my stats professor told the class that The New England
Journal of Medicine specified, 'Use /no/ p-levels' -- in an article
he co-authored, reporting the results of a health survey of 30,000
people. Anything big enough to be interesting would be 'significant'.
A number of non-interesting things also would be significant, at 0.05.
Years later, I analyzed a data set of similar size. I convinced the
PI that the F-tests of 245 and 350 were the ones that were
interesting. There were some ANOVA 2-way interactions that
that were p < 0.05 which were uninteresting -- some of them
were the consequence of 'non-linear scaling' across 3 or 4 points
on a rating scale, rather than any inherent interaction on the
latent dimension being measured. So, we only reported p< .001,
and (also) carefully dwelt on Effect sizes.
In the opposite direction -- In one study, we did report a
predicted one-tailed result at p < 0.05 for an interaction. The good
journal we submitted to accepted our 'one-tailed test' (both
'one-tailed' and 'interaction' suggest ugly data-dredging) only
because we could point to it as one of the (few) tests specified in
advance in our research proposal.
I liked the European standard that I heard of, long ago --
I don't know how wide-spread it is/was -- They reported the
"minimum N for which the test would be significant." I think
that people who are not statisticians can relate to this,
more easily that saying p < .05 and p< .001 or exact numbers.
An experimantal result that would be significant (.05) with N=10
is huge; one that requires N=500 is small.
(By the way, epidemiology results often /require/ huge N's
because of small effects as measured by Variance; that's why
their Effect sizes are reported as Odds ratios. 'Effect size' based
on N or power or p-level do not work well for rare outcomes.)
>
> Should a "complete" always include both statistical significance
> (p-value or alpha) and power ( 1-beta )? What are the "practical"
> meaning for power analysis? Say, would it be possible that the
> results are not significant, but of high power? What are the
> practical meaning for this situation?
As suggested elsewhere, high power gives a narrow Confidence
limit for the size of the actual effect in the experiment. Usually,
"very close to zero difference in means."
--
Rich Ulrich