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Current Incentives for Scientists Lead to Underpowered Studies with Erroneous Conclusions

机译:当前对科学家的激励导致结论不足的研究

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摘要

We can regard the wider incentive structures that operate across science, such as the priority given to novel findings, as an ecosystem within which scientists strive to maximise their fitness (i.e., publication record and career success). Here, we develop an optimality model that predicts the most rational research strategy, in terms of the proportion of research effort spent on seeking novel results rather than on confirmatory studies, and the amount of research effort per exploratory study. We show that, for parameter values derived from the scientific literature, researchers acting to maximise their fitness should spend most of their effort seeking novel results and conduct small studies that have only 10%–40% statistical power. As a result, half of the studies they publish will report erroneous conclusions. Current incentive structures are in conflict with maximising the scientific value of research; we suggest ways that the scientific ecosystem could be improved.
机译:我们可以将跨科学运作的更广泛的激励结构(例如对新发现给予优先考虑)视为科学家努力在其中最大化自身适应性(即出版记录和职业成就)的生态系统。在这里,我们开发了一个最优模型,该模型可以预测最合理的研究策略,其依据是用于寻找新颖结果而不是用于确认研究的研究工作所占的比例,以及每个探索性研究的工作量。我们表明,对于来自科学文献的参数值,研究人员应采取最大的努力来寻求新的结果,并进行只有10%至40%统计功效的小型研究。结果,他们发表的研究中有一半会报告错误的结论。当前的激励结构与最大化研究的科学价值相矛盾;我们提出了改善科学生态系统的方法。

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