But isn't planning a usable sample size part of sitting down and planning a well-designed experiment? I think it's emphasized too much as the only thing that matters because there are definitely aspects of a study designed that don't get considered nearly as much as they should but sample sizes still plays an important part don't you think?
yeah... i guess i didnt explain myself correctly there. you certainly have a point in the fact that you need to have some idea of how many people, dogs, galaxies, neurons or whatever you're gonna measure but, as you said, at least here in the world of social/behavioural/health sciences, people line outside my office asking for power analysis this and power analysis that and if they're "guaranteed" to get significance if they can get their sample size right and i'm like "no, no, no... focus on your research, on what you should be doing... the research question should guide the analysis and not vice-versa" now, i understand where they're coming from in a way because nowadays a lot of people ask for power stuff to even consider an application for funding, but still... it's weird here how people sometimes grab on to something for dear life and change it into something it was not meant for (like factor analysis, people pretty much get married to their factors)... one aspect that i would love people pay more attention to are confounders or missing variables instead of only sample size and statistical significance... i've been doing a lot of research on suppression effects in multiple regression and i think it's surprising, at least to me, the lengths people go to dump or just blatantly ignore suppression effects in the social sciences...
how's the RA position going, btw?