Remember, in research it is easy to conclude that some treatment does no good -- just do sloppy statistics. E.g., collect some data, flip a coin, and report the results of the coin flip. That will work because the coin flip is, in probability, independent of everything else including the data and treatment. Well that's not the only way to do sloppy statistics -- just pick some measures that are relevant but not very good. And use a small sample size.
E.g., there are some reports on how much data was needed by the Large Hadron Collider (LHC) to conclude that the Higgs Boson DOES exist -- the amount of data needed was beyond belief. Sooooo, anytime well short of that much data they could have conluded that the Higgs did not exist, that is, so far their data failed to show that it DID exist. Same for any treatment: If don't collect enough data, use poor measures, let the data get corrupted, etc., i.e., do sloppy work, then will fail to find where the treatment did work and conclude that it didn't.
E.g., there are some reports on how much data was needed by the Large Hadron Collider (LHC) to conclude that the Higgs Boson DOES exist -- the amount of data needed was beyond belief. Sooooo, anytime well short of that much data they could have conluded that the Higgs did not exist, that is, so far their data failed to show that it DID exist. Same for any treatment: If don't collect enough data, use poor measures, let the data get corrupted, etc., i.e., do sloppy work, then will fail to find where the treatment did work and conclude that it didn't.