The primary technique that many researchers use to analyze data from randomized control trials (RCTs)--detecting the average treatment effect (ATE)--imposes assumptions upon the data that often are not correct. Both theory and past research suggest that treatments may have significant impacts on subgroups even when showing no overall effect. Giving primacy to ATEs thus may lead to the rejection of treatments that in fact are helpful to some people. Using simulations, I examine the power of ATEs to detect treatment impacts when treatment impacts vary. Models that allow for varying impacts accurately measure the treatment effect in the simulation and are robust in a variety of circumstances. However, a focus on ATEs often fails to find the known treatment effects.