  Replication studies are increasingly conducted in order to confirm original
findings. However, there is no established standard how to assess replication
success and in practice many different approaches are used. The purpose of this
paper is to refine and extend a recently proposed reverse-Bayes approach for
the analysis of replication studies. We show how this method is directly
related to the relative effect size, the ratio of the replication to the
original effect estimate. This perspective leads to a new proposal to
recalibrate the assessment of replication success, the golden level. The
recalibration ensures that for borderline significant original studies
replication success can only be achieved if the replication effect estimate is
larger than the original one. Conditional power for replication success can
then take any desired value if the original study is significant and the
replication sample size is large enough. Compared to the standard approach to
require statistical significance of both the original and replication study,
replication success at the golden level offers uniform gains in project power
and controls the Type-I error rate if the replication sample size is not
smaller than the original one. An application to data from four large
replication projects shows that the new approach leads to more appropriate
inferences, as it penalizes shrinkage of the replication estimate compared to
the original one, while ensuring that both effect estimates are sufficiently
convincing on their own.
