This article introduces z-curve.2.0 as a method that estimates the expected replication rate (ERR) and the expected discovery rate (EDR) based on the test-statistics of studies selected for significance. Z-curve.2.0 extends the work by Brunner and Schimmack (2019) in several ways. First, we show that a new estimation method using expectation-maximization outperforms the kernel-density approach of z-curve.1.0. Second, we examine the coverage of bootstrapped confidence intervals to provide information about the uncertainty in z-curve estimates. Third, we extended z-curve to estimate the number of all studies that were conducted, including studies with non-significant results that may not have been reported, solely on the basis of significant results. This allows us to estimate the EDR; that is, the percentage of significant results that were obtained in all studies. EDR can be used to assess the size of the file-drawer, estimate the maximum number of false positive results, and may provide a better estimate of the success rate in actual replication studies than the ERR because exact replications are impossible.