Power Posing: P-Curving the Evidence

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p-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results.

Uri Simonsohn, Leif Nelson, Joseph Simmons (2014)

Journals tend to publish only statistically significant evidence, creating a scientific record that markedly overstates the size of effects. We provide a new tool that corrects for this bias without requiring access to nonsignificant results. It capitalizes on the fact that the distribution of significant p values, p-curve, is a function of the true underlying effect. Researchers armed only with sample sizes and test results of the published findings can correct for publication bias. We validate the technique with simulations and by reanalyzing data from the Many-Labs Replication project. We demonstrate that p-curve can arrive at conclusions opposite that of existing tools by reanalyzing the meta-analysis of the "choice overload" literature.

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Meta-regression approximations to reduce publication selection bias.

T Stanley, Hristos Doucouliagos (2014)

Publication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with standard error (PEESE), is shown to have the smallest bias and mean squared error in most cases and to outperform conventional meta-analysis estimators, often by a great deal. Monte Carlo simulations also demonstrate how a new hybrid estimator that conditionally combines PEESE and the Egger regression intercept can provide a practical solution to publication selection bias. PEESE is easily expanded to accommodate systematic heterogeneity along with complex and differential publication selection bias that is related to moderator variables. By providing an intuitive reason for these approximations, we can also explain why the Egger regression works so well and when it does not. These meta-regression methods are applied to several policy-relevant areas of research including antidepressant effectiveness, the value of a statistical life, the minimum wage, and nicotine replacement therapy.

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Is the Replicability Crisis Overblown? Three Arguments Examined.

Harold Pashler, Christine Harris (2012)

We discuss three arguments voiced by scientists who view the current outpouring of concern about replicability as overblown. The first idea is that the adoption of a low alpha level (e.g., 5%) puts reasonable bounds on the rate at which errors can enter the published literature, making false-positive effects rare enough to be considered a minor issue. This, we point out, rests on statistical misunderstanding: The alpha level imposes no limit on the rate at which errors may arise in the literature (Ioannidis, 2005b). Second, some argue that whereas direct replication attempts are uncommon, conceptual replication attempts are common-providing an even better test of the validity of a phenomenon. We contend that performing conceptual rather than direct replication attempts interacts insidiously with publication bias, opening the door to literatures that appear to confirm the reality of phenomena that in fact do not exist. Finally, we discuss the argument that errors will eventually be pruned out of the literature if the field would just show a bit of patience. We contend that there are no plausible concrete scenarios to back up such forecasts and that what is needed is not patience, but rather systematic reforms in scientific practice.

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Journal

Title: Psychological Science

Abbreviated Title: Psychological Science

Publisher: SAGE Publications

ISSN (Print): 0956-7976

ISSN (Electronic): 1467-9280

Publication date Created: March 20 2017

Publication date (Print): March 20 2017

Volume: 28

Issue: 5

Pages: 687-693

Article

DOI: 10.1177/0956797616658563

PubMed ID: 28485698

SO-VID: aca91a12-fdce-4921-ab02-3a68b1ac47d1

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Power Posing: P-Curving the Evidence

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IET Power Engineering

Most cited references 14

p-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results.

Meta-regression approximations to reduce publication selection bias.

Is the Replicability Crisis Overblown? Three Arguments Examined.

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