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Author: Uri, Joe, & Leif

[61] Why p-curve excludes ps>.05

Posted on June 15, 2017February 12, 2020 by Uri, Joe, & Leif

In a recent working paper, Carter et al (htm) proposed that one can better correct for publication bias by including not just p<.05 results, the way p-curve does, but also p>.05 results [1]. Their paper, currently under review, aimed to provide a comprehensive simulation study that compared a variety of bias-correction methods for meta-analysis. Although the…

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[49] P-Curve Won’t Do Your Laundry, But Will Identify Replicable Findings

Posted on June 14, 2016February 12, 2020 by Uri, Joe, & Leif

In a recent critique, Bruns and Ioannidis (PlosONE 2016 .htm) 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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[45] Ambitious P-Hacking and P-Curve 4.0

Posted on January 14, 2016February 11, 2020 by Uri, Joe, & Leif

In this post, we first consider how plausible it is for researchers to engage in more ambitious p-hacking (i.e., past the nominal significance level of p<.05). Then, we describe how we have modified p-curve (see app 4.0) to deal with this possibility. Ambitious p-hacking is hard. In “False-Positive Psychology” (SSRN), we simulated the consequences of four…

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[44] AsPredicted: Pre-registration Made Easy

Posted on December 1, 2015February 11, 2020 by Uri, Joe, & Leif

Pre-registering a study consists of leaving a written record of how it will be conducted and analyzed. Very few researchers currently pre-register their studies. Maybe it’s because pre-registering is annoying. Maybe it's because researchers don't want to tie their own hands. Or maybe it's because researchers see no benefit to pre-registering.  This post addresses these…

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[30] Trim-and-Fill is Full of It (bias)

Posted on December 3, 2014November 13, 2024 by Uri, Joe, & Leif

Statistically significant findings are much more likely to be published than non-significant ones (no citation necessary). Because overestimated effects are more likely to be statistically significant than are underestimated effects, this means that most published effects are overestimates. Effects are smaller – often much smaller – than the published record suggests. For meta-analysts the gold…

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[10] Reviewers are asking for it

Posted on December 9, 2013February 11, 2020 by Uri, Joe, & Leif

Recent past and present The leading empirical psychology journal, Psychological Science, will begin requiring authors to disclose flexibility in data collection and analysis starting on January of 2014 (see editorial). The leading business school journal, Management Science, implemented a similar policy a few months ago. Both policies closely mirror the recommendations we made in our…

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[7] Forthcoming in the American Economic Review: A Misdiagnosed Failure-to-Replicate

Posted on November 11, 2013February 11, 2020 by Uri, Joe, & Leif

In the paper “One Swallow Doesn't Make A Summer: New Evidence on Anchoring Effects”, forthcoming in the AER, Maniadis, Tufano and List attempted to replicate a classic study in economics. The results were entirely consistent with the original and yet they interpreted them as a “failure to replicate.” What went wrong? This post answers that…

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