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犯错中求对:一个有关错误研究如何最终自我实现的案例研究。

On getting it right by being wrong: A case study of how flawed research may become self-fulfilling at last.

机构信息

Max Planck Institute for Research on Collective Goods, 53113 Bonn, Germany.

EBS Law School, 65189 Wiesbaden, Germany.

出版信息

Proc Natl Acad Sci U S A. 2022 Apr 12;119(15):e2122274119. doi: 10.1073/pnas.2122274119. Epub 2022 Apr 8.

DOI:10.1073/pnas.2122274119
PMID:35394869
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9169707/
Abstract

Scientists prominently argue that the COVID-19 pandemic stems not least from people’s inability to understand exponential growth. They increasingly cite evidence from a classic psychological experiment published some 45 years prior to the first case of COVID-19. Despite—or precisely because of—becoming such a canonical study (more often cited than read), its critical design flaws went completely unnoticed. They are discussed here as a cautionary tale against uncritically enshrining unsound research in the “lore” of a field of research. In hindsight, this is a unique case study of researchers falling prey to just the cognitive bias they set out to study—undermining an experiment’s methodology while, ironically, still supporting its conclusion.

摘要

科学家们明确指出,COVID-19 大流行至少源于人们无法理解指数增长。他们越来越多地引用 COVID-19 首例病例发生前约 45 年前发表的一项经典心理学实验的证据。尽管(或者正是因为)该实验成为如此经典的研究(引用次数多于阅读次数),但其关键设计缺陷却完全被忽视了。本文将其作为一个警示故事,告诫人们不要不加批判地将有缺陷的研究奉为研究领域的“传说”。事后看来,这是一个研究人员恰好陷入他们试图研究的认知偏差的独特案例研究——在破坏实验方法的同时,讽刺的是,仍然支持其结论。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f71/9169707/a137b555e3a9/pnas.2122274119fig02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f71/9169707/13891954afec/pnas.2122274119fig01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f71/9169707/a137b555e3a9/pnas.2122274119fig02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f71/9169707/13891954afec/pnas.2122274119fig01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f71/9169707/a137b555e3a9/pnas.2122274119fig02.jpg

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Anticipating trajectories of exponential growth.预测指数增长轨迹。
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Misinformation in and about science.科学中的错误信息和围绕科学的错误信息。
Proc Natl Acad Sci U S A. 2021 Apr 13;118(15). doi: 10.1073/pnas.1912444117.
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COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease.COVID-19:一种用于预测疾病指数增长阶段重症监护病房负荷的简单统计模型。
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