Data volume and power of multiple tests with small sample size per null
  In multiple hypothesis testing, the volume of data, defined as the number of replications per null times the total number of nulls, usually defines the amount of resource required. On the other hand, power is an important measure of performance for multiple testing. Due to practical constraints, the number of replications per null may not be large enough in terms of the difference between false and true nulls. For the case where the population fraction of false nulls is constant, we show that, as the difference between false and true nulls becomes increasingly subtle while the number of replications per null cannot increase fast enough, (1) in order to have enough chance that the data to be collected will yield some trustworthy findings, as measured by a conditional version of the positive false discovery rate (pFDR), the volume of data has to grow at a rate much faster than in the case where the number of replications per null can be large enough, and (2) in order to control the pFDR asymptotically, power has to decay to 0 in a rate highly sensitive to rejection criterion and there is no asymptotically most powerful procedures among those that control the pFDR asymptotically at the same level. 
Zhiyi Chi