I am a bit confused -- is the framework for this thread observation (where the number of samples is pretty much the only thing you can affect pre-analysis) or experiment design (where you you can greatly affect which data you collect)?
I ask because I'm intrigued by the idea of trading off Type I errors against Type II errors, but I'm not sure it's possible in the observation context without introducing bias.
I'm not sure about this observation vs experiment design dichotomy you're thinking of. I think of power analysis as something which can be done both before an experiment to design it and understand what the data could tell one, and post hoc, to understand why you did or did not get a result and to estimate things for designing the next experiment.
If it's worth saying, but not worth its own post (even in Discussion), then it goes here.