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GOODMAN: When You’re Selecting Significant Findings, You’re Selecting Inflated Estimates

Replication researchers cite inflated effect sizes as a major cause of replication failure. It turns out this is an inevitable consequence of significance testing. The reason is simple. The p-value you get from a study depends on the observed effect…

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What If There Isn’t a Single Effect Size? Implications for Power Calculations, Hypothesis Testing, Confidence Intervals and Replications

[From the working paper “The Unappreciated Heterogeneity of Effect Sizes:Implications for Power, Precision, Planning of Research, and Replication” by David Kenny and Charles Judd, posted at Open Science Framework (OSF)] “The goal of this article is to examine the implications…

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BROWN, LAMBERT, & WOJAN: At the Intersection of Null Findings and Replication

Replication is an important topic in economic research or any social science for that matter. This issue is most important when an analysis is undertaken to inform decisions by policymakers. Drawing inferences from null or insignificant finding is particularly problematic…

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