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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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Lack of selectivity for syntax relative to word meanings throughout the language network.

TL;DR: Contrary to many current proposals of the neural architecture of language, syntactic/combinatorial processing is not separable from lexico-semantic processing at the level of brain regions-or even voxel subsets-within the language network, in line with strong integration between these two processes that has been consistently observed in behavioral and computational language research.
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Using reinforcement learning models in social neuroscience: frameworks, pitfalls and suggestions of best practices.

TL;DR: A comprehensive framework for the examination of (social) decision-making with the simple Rescorla–Wagner RL model is presented and posterior predictive check is a crucial step after model comparison, and employing hierarchical modeling for parameter estimation is suggested.
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Common misconceptions about data analysis and statistics

TL;DR: The reproducibility of a large percentage of published findings has been questioned, and one reason may be that investigators fool themselves due to a poor understanding of statistical concepts.
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When both the original study and its failed replication are correct: Feeling observed eliminates the facial-feedback effect

TL;DR: A theoretically driven explanation for a replication failure of one of the basic findings in psychology: the facial-feedback effect is suggested and minute differences in the experimental protocol might lead to theoretically meaningful changes in the outcomes.
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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