Not necessarily in the Bayesian framework, though it's kinda natural there. You can think in terms of complete distributions within the frequentist framework perfectly well, too.
The issue that we started with was of statistical power, right? While it's technically defined in terms of the usual significance (=rejecting the null hypothesis), you can think about it in broader terms. Essentially it's the capability to detect a signal (of certain effect size) in the presence of noise (in certain amounts) with a given level of confidence.
Thank for the paper, I've seen it before but didn't have a handy link to it.
You can think in terms of complete distributions within the frequentist framework perfectly well, too.
Does anyone do that, though?
Essentially it's the capability to detect a signal (of certain effect size) in the presence of noise (in certain amounts) with a given level of confidence.
Well, if you want to think of it like that, you could probably formulate all of this in information-theoretic terms and speak of needing a certain number of bits; then the sample size & effect size interact to say how many bits each n contains. So a binary variable ...
If it's worth saying, but not worth its own post (even in Discussion), then it goes here.