Expectations for Replications

Abstract

Failures to replicate published psychological research findings have contributed to a “crisis of confidence.” Several reasons for these failures have been proposed, the most notable being questionable research practices and data fraud. We examine replication from a different perspective and illustrate that current intuitive expectations for replication are unreasonable. We used computer simulations to create thousands of ideal replications, with the same participants, wherein the only difference across replications was random measurement error. In the first set of simulations, study results differed substantially across replications as a result of measurement error alone. This raises questions about how researchers should interpret failed replication attempts, given the large impact that even modest amounts of measurement error can have on observed associations. In the second set of simulations, we illustrated the difficulties that researchers face when trying to interpret and replicate a published finding. We also assessed the relative importance of both sampling error and measurement error in producing variability in replications. Conventionally, replication attempts are viewed through the lens of verifying or falsifying published findings. We suggest that this is a flawed perspective and that researchers should adjust their expectations concerning replications and shift to a meta-analytic mind-set.