Again, academia/research is not my profession. But, some cool efforts in this area include osf.io, which is trying to be the Arxiv or Github of preregistration for scientific studies.
The best preregistration plans will typically include a declared sample or population to observe (http://datacolada.org/64), or at least clear cut criteria for which participants or observations you will exclude.
I think for the type of economics/finance research I’m most familiar with, you often implicitly announce your sample when securing funding for a research proposal. E.g. if I’m trying to see if pursuing a momentum strategy with S&P 500 stocks is profitable (a la AQR’s work), it’s pretty obvious what the sample ought to be. This is partly why that meta study I linked to earlier was able to sniff out potential signs of p-hacking.
The best preregistration plans will typically include a declared sample or population to observe (http://datacolada.org/64), or at least clear cut criteria for which participants or observations you will exclude.
I think for the type of economics/finance research I’m most familiar with, you often implicitly announce your sample when securing funding for a research proposal. E.g. if I’m trying to see if pursuing a momentum strategy with S&P 500 stocks is profitable (a la AQR’s work), it’s pretty obvious what the sample ought to be. This is partly why that meta study I linked to earlier was able to sniff out potential signs of p-hacking.