Why Most Published Research Findings Are FalseJohn P. A. Ioannidis
Erstpublikation in: PLoS Med 2(8): e124.
Publikationsdatum:
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Zusammenfassungen
Published research findings are
sometimes refuted by subsequent
evidence, with ensuing confusion
and disappointment. Refutation and
controversy is seen across the range of
research designs, from clinical trials
and traditional epidemiological studies
[1–3] to the most modern molecular
research [4,5]. There is increasing
concern that in modern research, false
fi ndings may be the majority or even
the vast majority of published research
claims [6–8]. However, this should
not be surprising. It can be proven
that most claimed research findings
are false. Here I will examine the key
factors that influence this problem and
some corollaries thereof.
Von John P. A. Ioannidis im Text Why Most Published Research Findings Are False (2005) 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.
Von John P. A. Ioannidis im Text Why Most Published Research Findings Are False (2005)
Dieser wissenschaftliche Zeitschriftenartikel erwähnt ...
![]() Begriffe KB IB clear | Effektstärkeeffect size, Metaanalysemeta-analysis, Signifikanz, Simulation, ![]() ![]() |
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3 Erwähnungen 
- The Essential Guide to Effect Sizes - Statistical Power, Meta-Analysis, and the Interpretation of Research Results (Paul D. Ellis) (2010)
- The Signal and the Noise (Nate Silver) (2012)
- The Chrysalis Effect - How Ugly Initial Results Metamorphosize Into Beautiful Articles (2014)
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