Great article. Determining an appropriate sample size - or put another way, determining the ramifications of a sample size on the strengths and limitations of a study - is maybe the most overlooked aspect of analyses of observational data.
Most people can come up with a "low" number which their study should surpass to have adequate power, or can at least identify situations when they are lacking sample size (if they haven't performed the calculation a priori - "Hmm, those confidence intervals are pretty large...I bet small sample size is why my results are not significant).
But how can a researcher determine his "high" end number? In other words, how big of a sample is too big? With secondary data analysis this is often an issue (or should be), especially with national level data with lots of observations.
You suggested earlier considering the practical significance of the difference in question. I wonder if a researcher anticipating a too-large sample size problem could do this but in the opposite direction that is typical. "What size difference do I NOT want to be able to detect?" In other words, how small of a difference would not be practically significant, and therefore what should the upper limit of my sample size be to guarantee that I will not find such a difference to be significant?
I think this would be a sound strategy, except that researchers are usually TRYING to find a difference, so keeping themselves from finding a significant difference, no matter how small that difference is, may be unreasonable to expect.