We often hear of studies with "too low" a sample size. But what does that actually mean? Is it perhaps any of these two things (if not, please suggest a better interpretation):
1) Inability to reject a certain null hypothesis, given a certain expected effect size and power level.
2) Too poor an estimate of the population mean given the sample mean. (see follow-up questions on this below)
2.1) How is a "good enough" such estimate defined?!
2.2) Does the computation of such an estimate qualify as inference or as interval estimation, and is it not true that computing confidence intervals achieves both aims? I ask because I know that advocates of statistical reform suggest emphasis should swing from NHST to statistical estimation, but I don't see how one could do one without the other
I apologise for this question mixing up several different questions, but I found no way to ask in a way that disambiguates the different issues.