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False-Positive Psychology
TLDR
It is shown that despite empirical psychologists’ nominal endorsement of a low rate of false-positive findings, flexibility in data collection, analysis, and reporting dramatically increases actual false- positive rates, and a simple, low-cost, and straightforwardly effective disclosure-based solution is suggested.
Small Telescopes
This article introduces a new approach for evaluating replication results. It combines effect-size estimation with hypothesis testing, assessing the extent to which the replication results are
P-Curve: A Key to the File Drawer
TLDR
By telling us whether the authors can rule out selective reporting as the sole explanation for a set of findings, p-curve offers a solution to the age-old inferential problems caused by file-drawers of failed studies and analyses.
A manifesto for reproducible science
TLDR
This work argues for the adoption of measures to optimize key elements of the scientific process: methods, reporting and dissemination, reproducibility, evaluation and incentives, in the hope that this will facilitate action toward improving the transparency, reproducible and efficiency of scientific research.
Promoting an open research culture
Author guidelines for journals could help to promote transparency, openness, and reproducibility Transparency, openness, and reproducibility are readily recognized as vital features of science (1,
Small Telescopes: Detectability and the Evaluation of Replication Results
This paper introduces a new approach for evaluating replication results. It combines effect-size estimation with hypothesis testing, assessing the extent to which the replication results are
Data from Paper “False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant”
The data includes measures collected for the two experiments reported in “False-Positive Psychology” [1] where listening to a randomly assigned song made people feel younger (Study 1) or actually be
p-Curve and Effect Size
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
Specification Curve: Descriptive and Inferential Statistics on All Reasonable Specifications
Empirical results often hinge on data analytic decisions that are simultaneously defensible, arbitrary, and motivated. To mitigate this problem we introduce Specification-Curve Analysis, which
Better P-curves: Making P-curve analysis more robust to errors, fraud, and ambitious P-hacking, a Reply to Ulrich and Miller (2015).
TLDR
This work considers the possibility that researchers report only the smallest significant p value, the impact of more common problems, including p-curvers selecting the wrong p values, fake data, honest errors, and ambitiously p-hacked results, and provides practical solutions that substantially increase its robustness.
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