Replication is often viewed as the demarcation between science and nonscience. However, contrary to the commonly held view, we show that in the current (selective) publication system replications may increase bias in effect size estimates. Specifically, we examine the effect of replication on bias in estimated population effect size as a function of publication bias and the studies’ sample size...

This paper addresses the use of weight functions to model publication bias in meta-analysis. Since this bias is hard to gauge, we introduce a non-parametric "-contamination class of weight functions. We then illustrate how to explore sensitivity of conclusions to the speci cation of the weight function by examining the range of results for the entire class. We nd lower bounds on the coverage of...

Replication is often viewed as the demarcation between science and non-science. However, contrary to the commonly held view, we show that in the current (selective) publication system replications may increase bias in effect size estimates. Specifically, we examine the effect of replication on bias in estimated population effect size as a function of publication bias and the studies’ sample siz...

background: the aim of the study was assessment of defaults and conducted meta-analysis of the efficacy of single-dose oral albendazole against t. trichiura infection. methods: we searched pubmed, isi web of science, science direct, the cochrane central register of controlled trials, and who library databases between 1983 and 2014. data from 13 clinical trial ar­ticles were used. each article w...

BACKGROUND Publication bias compromises the validity of evidence-based medicine, yet a growing body of research shows that this problem is widespread. Efficacy data from drug regulatory agencies, e.g., the US Food and Drug Administration (FDA), can serve as a benchmark or control against which data in journal articles can be checked. Thus one may determine whether publication bias is present an...

Prior retrieval practice potentiates new learning. A recent meta-analysis of this test-potentiated learning (TPNL) effect by Chan, Meissner, and Davis (2018) concluded that it is a robust reliable finding (Hedges’ g = 0.44). Although Chan et al. discussed three different experimental designs have been employed to study TPNL, we argue their failed adequately distinguish the findings from these d...