Beyond Power Calculations

By Andrew Gelman, J. Carlin. Perspectives on Psychological Science, 2014

925 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, General Psychology

View Paper: https://www.semanticscholar.org/paper/9ea3cd1a9344513d10c45f2bb4762db76e194d0f

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In Experimental and Cognitive Psychology

In General Psychology

Citations in the corpus

Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses (2019, neutral): “2013, Gelman and Carlin 2014, Lemoine et al.”
Single Paper Meta-Analysis: Benefits For Study Summary, Theory-Testing, and Replicability (2017, background): “…it helps avoid the upwardly biased estimates of effect sizes and downwardly biased estimates of heterogeneity that generally result from the selective reporting of studies that attain statistical significance (Gelman and Carlin 2014; Gelman and Weakliem 2009; McShane, Böckenholt, and Hansen 2016).”
I can see clearly now: Reinterpreting statistical significance (2018, background): “…real world benefits, novelty and other factors) (McShane et al., 2017) and reporting of a priori analysis of statistical power to avoid emphasis on implausibly large effects given low statistical power (the “winner’s curse” Gelman and Carlin, 2014, Szucs and Ioannidis, 2017, Bernardi et al., 2017).”
Meaningful Mediation Analysis: Plausible Causal Inference and Informative Communication (2017, background): “Retrospective power analysis “often seems to be used as an alibi to explain away nonsignificant findings” (Gelman and Carlin 2014, 2).”
Reproducible research in sport and exercise psychology: The role of sample sizes (2016, methodology): “This is because they lead to imprecise estimations of effect sizes (e.g., Cumming, 2012; Gelman & Carlin, 2014; Sch€onbrodt & Perugini, 2013).”
Effectiveness of spelling interventions for learners with dyslexia: A meta-analysis and systematic review (2020, background): “It is well known that publication bias and questionable research practices inflate effect size estimates of meta-analyses (Gelman & Carlin, 2014; Gelman & Loken, 2014; Schmidt, 1992).”
The relation between statistical power and inference in fMRI (2017, methodology): “These results are strongly related to statistical power: in other words, underpowered studies will be less likely to replicate [24].”
Power Analysis and Effect Size in Mixed Effects Models: A Tutorial (2018, background): “…problem combined with low power not only results in published papers with effects that do not exist at the population level, but also leads to an unacceptably high number of publications reporting and interpreting effects opposite to the one present at the population level (Gelman & Carlin, 2014).”
The significance filter, the winner's curse and the need to shrink (2020, methodology): “We define the relative conditional bias as E(|b||se, 𝛽, |b|∕se > c) − |𝛽||𝛽| , and the exaggeration ratio or type M error (Gelman & Carlin 2014) as E(|b||se, 𝛽, |b| > c)∕|𝛽|.”