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.
%0 Journal Article
%1 Ioannidis2005
%A a Ioannidis, John P
%D 2005
%J PLoS medicine
%K (Epidemiology),Data Bias Design,Sample Functions,Meta-Analysis Interpretation, Ratio,Publishing,Reproducibility Results,Research Size Statistical,Likelihood Topic,Odds as of
%N 8
%P e124
%R 10.1371/journal.pmed.0020124
%T Why most published research findings are false.
%U http://www.ncbi.nlm.nih.gov/pubmed/16060722
%V 2
%X 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.
@article{Ioannidis2005,
abstract = {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.},
added-at = {2011-03-27T17:20:41.000+0200},
author = {a Ioannidis, John P},
biburl = {https://www.bibsonomy.org/bibtex/2697a9815e54a8272fe66df10b70a93c4/yevb0},
doi = {10.1371/journal.pmed.0020124},
interhash = {cd0cb52b4896ec04f2f66972c325354e},
intrahash = {697a9815e54a8272fe66df10b70a93c4},
issn = {1549-1676},
journal = {PLoS medicine},
keywords = {(Epidemiology),Data Bias Design,Sample Functions,Meta-Analysis Interpretation, Ratio,Publishing,Reproducibility Results,Research Size Statistical,Likelihood Topic,Odds as of},
month = aug,
number = 8,
pages = {e124},
pmid = {16060722},
timestamp = {2011-03-27T17:20:54.000+0200},
title = {Why most published research findings are false.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16060722},
volume = 2,
year = 2005
}