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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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Citations
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Priming Intelligent Behavior: An Elusive Phenomenon

TL;DR: None of the experiments obtained the intelligence priming effect, although financial incentives did boost performance, and a Bayesian analysis reveals considerable evidential support for the null hypothesis.
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Business Not as Usual

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TL;DR: The scope of these five initiatives on word limits, evaluation criteria, methodological reports, open practices, and “new” statistics is sketched here, along with the reasoning behind them.
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Perceived stress and telomere length: A systematic review, meta-analysis, and methodologic considerations for advancing the field.

TL;DR: A very small, statistically significant relationship between increased PS (as measured over the past month) and decreased TL is found that may reflect publication bias, although fully parsing the effects of publication bias from other sample-size correlates is challenging, as discussed.
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Best practice guidance for linear mixed-effects models in psychological science

TL;DR: The use of linear mixed-effects models (LMMs) is set to dominate statistical analyses in psychological science and may become the default approach to analyzing quantitative data as discussed by the authors, however, there has been a proliferation of differences in practice.
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.
Journal ArticleDOI

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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