It is a paradox of statistics that experiments with small sample sizes are more likely to find larger effects of an intervention, when they find anything at all. This happens because smaller samples produce noisier estimates. For a result to clear the significance bar in a noisy environment, the observed effect has to be large. So the significance test ends up acting as a filter that systematically lets through the most extreme estimates: precisely the ones most likely inflated by random variation.  This is what we call the winner's curse: the point estimate that clears the bar in a small sample tends to grossly overestimate the real impact. In other words, if you only look at "statistically significant" results from…