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好科学的自然选择。

The natural selection of good science.

机构信息

School of Mathematics and Statistics, University of St Andrews, St Andrews, UK.

Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.

出版信息

Nat Hum Behav. 2021 Nov;5(11):1510-1518. doi: 10.1038/s41562-021-01111-x. Epub 2021 May 17.

Abstract

Scientists in some fields are concerned that many published results are false. Recent models predict selection for false positives as the inevitable result of pressure to publish, even when scientists are penalized for publications that fail to replicate. We model the cultural evolution of research practices when laboratories are allowed to expend effort on theory, enabling them, at a cost, to identify hypotheses that are more likely to be true, before empirical testing. Theory can restore high effort in research practice and suppress false positives to a technical minimum, even without replication. The mere ability to choose between two sets of hypotheses, one with greater prior chance of being correct, promotes better science than can be achieved with effortless access to the set of stronger hypotheses. Combining theory and replication can have synergistic effects. On the basis of our analysis, we propose four simple recommendations to promote good science.

摘要

一些领域的科学家担心,许多已发表的研究结果是错误的。最近的模型预测,即使科学家因未能复制的出版物而受到惩罚,发表虚假阳性结果也会成为发表压力下不可避免的结果。当实验室被允许在理论上投入精力时,我们可以对研究实践的文化进化进行建模,使它们能够在进行经验测试之前,付出代价地识别出更有可能正确的假设。即使没有复制,理论也可以恢复研究实践中的高投入,并将假阳性抑制到技术上的最低限度。仅仅能够在两个假设集之间进行选择,一个假设具有更大的正确可能性,就可以促进比轻松获得更强假设集更好的科学。理论和复制的结合可以产生协同效应。基于我们的分析,我们提出了四项简单的建议,以促进良好的科学研究。

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