be smaller than two thresholds. In an extreme scenario where all hypotheses are from I$_{10}$ or I$_{01}$ , and the p -values from false null hypotheses are zero, the two stage procedure may have an FDR of one, as follows. The BH procedure on the primary study p -values will reject all hypotheses from I$_{10}$ but also few from I$_{01}$ (when | I$_{01}$ | and | I$_{10}$ | are large enough), and the hypotheses from I$_{01}$ will be rejected by the BH procedure on the follow-up study p -values, resulting in an FDR of one. However, Procedure 3 . 2 will have an FDR level below q . To see this, note that in order to reject a no replicability null hypothesis by Procedure 3.2, the p -value of the Simes test (Simes, 1986) for the intersection of the elementary hypotheses indexed by I$_{01}$ , using the data from the primary study, has to be below q$_{1}$ , or the Simes test p -value for the intersection of elementary hypotheses indexed by I$_{10}$ ∩ R $_{1}$, using the data from the follow-up study, has to be below q − q$_{1}$ . Therefore, the probability of rejecting at least one no replicability null hypothesis, which coincides with the FDR since all no replicability null hypotheses are true, is at most q . See Figure 3, right panel, for a more realistic simulated example. Consider now a situation where both studies are available before the analysis. If some of the elementary hypotheses are examined in only one of the studies, then these hypotheses are not considered for replicability analysis. In this setting, there is no primary study and follow-up study. We propose the following generalization of Procedure 3.2, that can be tuned to treat the two studies symmetrically. Without loss of generality, we label the studies as study one and study two. Procedure 4.1. The generalized two stage procedure for testing a family of no 4 Replicability analysis with no division into pri- mary and follow-up studies 12