Why Most Published Research Findings Are False

Почему большинство опубликованных результатов исследований являются ложными
John P. A. Ioannidis
2005-08-02

multiple testingpublication biasresearch findingsresearcher degrees of freedomstudy power
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.
1
In many scientific fields, published findings may primarily reflect prevailing bias rather than accurate underlying relationships.
2
Research findings are less likely to be true with smaller studies, smaller effects, extensive testing, flexible methodologies, conflicts of interest, and many teams pursuing statistical significance.
3
Simulations indicate that across most study designs and settings, a reported research claim is more likely to be false than true.
4
The probability that a research claim is true depends on study power, bias, the number of studies, and the field-specific ratio of true to null relationships.

Published research findings and research claims across scientific fields

The probability of research claims being true versus false as a function of study power, bias, effect size, multiplicity and preselection of tested relationships, methodological flexibility, conflicts of interest, and team involvement

Publication Details
Publication Date
2005-08-02
Journal
Publisher
ISSN
Cited by
10790
Access Type
Author Information
Authors
John P. A. Ioannidis
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat →
Make a presentation
100%