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Meta-assessment of bias in science
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Numerous biases are believed to affect the scientific literature, but their actual prevalence across disciplines is unknown. To gain a comprehensive picture of the potential imprint of bias in science, we probed for the most commonly postulated bias-related patterns and risk factors, in a large random sample of meta-analyses taken from all disciplines. The magnitude of these biases varied widely across fields and was overall relatively small. However, we consistently observed a significant risk of small, early, and highly cited studies to overestimate effects and of studies not published in peer-reviewed journals to underestimate them. We also found at least partial confirmation of previous evidence suggesting that US studies and early studies might report more extreme effects, although these effects were smaller and more heterogeneously distributed across meta-analyses and disciplines. Authors publishing at high rates and receiving many citations were, overall, not at greater risk of bias. However, effect sizes were likely to be overestimated by early-career researchers, those working in small or long-distance collaborations, and those responsible for scientific misconduct, supporting hypotheses that connect bias to situational factors, lack of mutual control, and individual integrity. Some of these patterns and risk factors might have modestly increased in intensity over time, particularly in the social sciences. Our findings suggest that, besides one being routinely cautious that published small, highly-cited, and earlier studies may yield inflated results, the feasibility and costs of interventions to attenuate biases in the literature might need to be discussed on a discipline-specific and topic-specific basis.

Subject:
Applied Science
Biology
Health, Medicine and Nursing
Life Science
Physical Science
Social Science
Material Type:
Reading
Provider:
Proceedings of the National Academy of Sciences
Author:
Daniele Fanelli
John P. A. Ioannidis
Rodrigo Costas
Date Added:
08/07/2020
Signaling the trustworthiness of science
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CC BY
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Trust in science increases when scientists and the outlets certifying their work honor science’s norms. Scientists often fail to signal to other scientists and, perhaps more importantly, the public that these norms are being upheld. They could do so as they generate, certify, and react to each other’s findings: for example, by promoting the use and value of evidence, transparent reporting, self-correction, replication, a culture of critique, and controls for bias. A number of approaches for authors and journals would lead to more effective signals of trustworthiness at the article level. These include article badging, checklists, a more extensive withdrawal ontology, identity verification, better forward linking, and greater transparency.

Subject:
Life Science
Social Science
Material Type:
Reading
Provider:
Proceedings of the National Academy of Sciences
Author:
Kathleen Hall Jamieson
Marcia McNutt
Richard Sever
Veronique Kiermer
Date Added:
08/07/2020