Why most published research findings are false.

Abstract

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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.

PLoS Med. 2005 Aug;2(8):e124. Epub 2005 Aug 30.

Why most published research findings are false.

Ioannidis JP.

Comment in
Why most published research findings are false: problems in the analysis. [PLoS Med. 2007]
The clinical interpretation of research. [PLoS Med. 2005]
Power, reliability, and heterogeneous results. [PLoS Med. 2005]
Truth, probability, and frameworks. [PLoS Med. 2005]
Minimizing mistakes and embracing uncertainty. [PLoS Med. 2005]

Read more @ Why most published research findings are false. [PLoS Med. 2005] - PubMed - NCBI:

The Pundit: This paper underlines why we are right to be scientifically sceptical about claims that on prior evidence, are implausible or unlikely.

For the record, some other selected comments of Prof. Ioannidis:


1. An epidemic of false claims. Competition and conflicts of interest distort too many medical findings.
Ioannidis JP.
Sci Am. 2011 Jun;304(6):16. No abstract available.

2.
The false-positive to false-negative ratio in epidemiologic studies.
Ioannidis JP, Tarone R, McLaughlin JK.
Epidemiology. 2011 Jul;22(4):450-6.

3.
Risk factors and interventions with statistically significant tiny effects.
Siontis GC, Ioannidis JP.
Int J Epidemiol. 2011 Oct;40(5):1292-307. Epub 2011 Jul 6.

4.
Excess significance bias in the literature on brain volume abnormalities.
Ioannidis JP.
Arch Gen Psychiatry. 2011 Aug;68(8):773-80. Epub 2011 Apr 4.

5.
Statistically significant meta-analyses of clinical trials have modest credibility and inflated effects.
Pereira TV, Ioannidis JP.
J Clin Epidemiol. 2011 Oct;64(10):1060-9. Epub 2011 Mar 31. Review.

6.
Who is afraid of reviewers' comments? Or, why anything can be published and anything can be cited.
Ioannidis JP, Tatsioni A, Karassa FB.
Eur J Clin Invest. 2010 Apr;40(4):285-7. No abstract available.

7.
Science mapping analysis characterizes 235 biases in biomedical research.
Chavalarias D, Ioannidis JP.
J Clin Epidemiol. 2010 Nov;63(11):1205-15. Epub 2010 Apr 18.

8.
Overinterpretation of clinical applicability in molecular diagnostic research.
Lumbreras B, Parker LA, Porta M, Pollán M, Ioannidis JP, Hernández-Aguado I.
Clin Chem. 2009 Apr;55(4):786-94. Epub 2009 Feb 20.

9.
Why current publication practices may distort science.
Young NS, Ioannidis JP, Al-Ubaydli O.
PLoS Med. 2008 Oct 7;5(10):e201. No abstract available.

10.
Why most discovered true associations are inflated.
Ioannidis JP.
Epidemiology. 2008 Sep;19(5):640-8. Review. Erratum in: Epidemiology. 2009 Jul;20(4):629.

11.
Effect of formal statistical significance on the credibility of observational associations.
Ioannidis JP.
Am J Epidemiol. 2008 Aug 15;168(4):374-83; discussion 384-90. Epub 2008 Jul 8. Review.

12.
Perfect study, poor evidence: interpretation of biases preceding study design.
Ioannidis JP.
Semin Hematol. 2008 Jul;45(3):160-6.

13.
Some main problems eroding the credibility and relevance of randomized trials.
Ioannidis JP.
Bull NYU Hosp Jt Dis. 2008;66(2):135-9.

14.
Effectiveness of antidepressants: an evidence myth constructed from a thousand randomized trials?
Ioannidis JP.
Philos Ethics Humanit Med. 2008 May 27;3:14.

15.
Falsified papers in high-impact journals were slow to retract and indistinguishable from nonfraudulent papers.
Trikalinos NA, Evangelou E, Ioannidis JP.
J Clin Epidemiol. 2008 May;61(5):464-70.

16.
Persistence of contradicted claims in the literature.
Tatsioni A, Bonitsis NG, Ioannidis JP.
JAMA. 2007 Dec 5;298(21):2517-26.

17.
Why most published research findings are false: author's reply to Goodman and Greenland.
Ioannidis JP.
PLoS Med. 2007 Jun;4(6):e215. No abstract available.

18.
Limitations are not properly acknowledged in the scientific literature.
Ioannidis JP.
J Clin Epidemiol. 2007 Apr;60(4):324-9. Epub 2007 Jan 22.

19.
Open letter to the leader of academic medicine.
Ioannidis JP, Ahmed T, Awasthi S, Clarfield AM, Clark J, Dandona L, Howe A, Lozano JM, Li Y, Madani H, Marusic A, Mohammed I, Purcell GP, Rhoads M, Sliwa-Hähnle K, Straus SE, Edejer TT, Tugwell P, Ward R, Wilkes MS, Smith R.
BMJ. 2007 Jan 27;334(7586):191-3. No abstract available.



Where many claims go after the science journal/Press release
18 November 2012 Last updated at 00:54 GMT
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