P-Curve Won’t Do Your Laundry, But Will Identify Replicable Findings - Data Colada
In a recent critique, Bruns and Ioannidis (PlosONE 2016 .pdf) proposed that p-curve makes mistakes when analyzing studies that have collected field/observational data. They write that in such cases: p-curves based on true effects and p‑curves based on null-effects with p-hacking cannot be reliably distinguished” (abstract). In this post we show, with examples involving sex,...
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