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Power and Progress in Science

[From the blog “The Persistence of False Paradigms in Low-Power Sciences” by Pascal Michaillat, posted on the BITSS website] “It is commonly believed that the lack of experimental evidence typical in the social sciences slows but does not prevent the…

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Power is not the point. It’s the curve.

[From the blog “Why you shouldn’t say ‘this study is underpowered’” by Richard Morey, posted at Towards Data Science, at Medium. com] “The first thing to clear up, as I’ve stated above, is that study or an experiment is not…

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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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MILLER: The Statistical Fundamentals of (Non-)Replicability

“Replicability of findings is at the heart of any empirical science” (Asendorpf, Conner, De Fruyt, et al., 2013, p. 108) The idea that scientific results should be reliably demonstrable under controlled circumstances has a special status in science.  In contrast…

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How to Increase Power Without Increasing Sample Size

[From the blog “Power to the Plan” by Clare Leaver, Owen Ozier, Pieter Serneels, and Andrew Zeitlin, posted at BITSS] “…Our blinded pre-analytical work uncovered two decision margins that could deliver substantial increases in power: changing test statistics used and putting structure on…

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80% Power? Really?

[From the blog “The “80% power” lie” posted by Andrew Gelman in December 2017 at Statistical Modeling, Causal Inference, and Social Science] “Suppose we really were running studies with 80% power. In that case, the expected z-score is 2.8, and…

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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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