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False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant

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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.
Abstract
In this article, we accomplish two things. First, we show that despite empirical psychologists' nominal endorsement of a low rate of false-positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. We present computer simulations and a pair of actual experiments that demonstrate how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis. Second, we suggest a simple, low-cost, and straightforwardly effective disclosure-based solution to this problem. The solution involves six concrete requirements for authors and four guidelines for reviewers, all of which impose a minimal burden on the publication process.

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Sexual Orientation, Controversy, and Science:

TL;DR: Ongoing political controversies around the world exemplify a long-standing and widespread preoccupation with the acceptability of homosexuality, and the most contentious scientific issues have concerned the causes of sexual orientation.
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Comment on “Estimating the reproducibility of psychological science”

TL;DR: It is shown that this article contains three statistical errors and provides no support for the conclusion that the reproducibility of psychological science is surprisingly low, and that the data are consistent with the opposite conclusion.
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P-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results

TL;DR: The p-curve algorithm as discussed by the authors uses the distribution of significant p-values as a function of the true underlying effect to correct publication bias in the scientific record, which can be used to correct bias without access to nonsignificant results.
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The ironic effect of significant results on the credibility of multiple-study articles.

TL;DR: One major recommendation is to pay more attention to the power of studies to produce positive results without the help of questionable research practices and to request that authors justify sample sizes with a priori predictions of effect sizes.
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Religious Priming A Meta-Analysis With a Focus on Prosociality

TL;DR: Results show that religious priming has robust effects across a variety of outcome measures—prosocial measures included—suggesting that priming depends on the cognitive activation of culturally transmitted religious beliefs.
References
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Journal ArticleDOI

The case for motivated reasoning.

TL;DR: It is proposed that motivation may affect reasoning through reliance on a biased set of cognitive processes--that is, strategies for accessing, constructing, and evaluating beliefs--that are considered most likely to yield the desired conclusion.

Why Most Published Research Findings Are False

TL;DR: In this paper, the authors discuss the implications of these problems for the conduct and interpretation of research and suggest that claimed research findings may often be simply accurate measures of the prevailing bias.
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Group sequential methods in the design and analysis of clinical trials

TL;DR: In this article, a group sequential design is proposed to divide patient entry into a number of equal-sized groups so that the decision to stop the trial or continue is based on repeated significance tests of the accumulated data after each group is evaluated.
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Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling

TL;DR: It is found that the percentage of respondents who have engaged in questionable practices was surprisingly high, which suggests that some questionable practices may constitute the prevailing research norm.
Journal ArticleDOI

Attribution of success and failure revisited, or: The motivational bias is alive and well in attribution theory

TL;DR: The authors found that self-serving effects for both success and failure are obtained in most but not all experimental paradigms, and that these attributions are better understood in motivational than in information-processing terms.
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