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Most Published Research Findings False ?


Dr Ioannidis, who works at the University of Ioannina, in northern Greece, makes his claim in PLoS Medicine, an online journal published by the Public Library of Science. His thesis that many scientific papers come to false conclusions is not new. Science is a Darwinian process that proceeds as much by refutation as by publication. But until recently no one has tried to quantify the matter.

Dr Ioannidis began his study by reviewing 49 research articles printed in widely read medical journals between 1990 and 2003. Each of these articles had been cited by other scientists in their own papers 1,000 times or more. However, 14 of them—almost a third—were later refuted by other work. Having established the reality of his point, he then designed a mathematical model that tried to take into account and quantify sources of error. Again, these are well known in the field.

One is an unsophisticated reliance on “statistical significance”. To qualify a result as statistically significant, the probability that it is the result of pure coincidence should be smaler than 1:20. But, as Dr Ioannidis points out, adhering to this standard means that simply examining 20 different hypotheses at random is likely to give you one statistically significant result. In fields where thousands of possibilities have to be examined, such as the search for genes that contribute to a particular disease, many seemingly meaningful results are bound to be wrong just by chance.

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.

When Dr Ioannidis ran the numbers through a simulation, his model predicted that even a large, well-designed study with little researcher bias has only an 85% chance of being right. An underpowered, poorly performed drug trial with researcher bias has but a 17% chance of producing true conclusions. Overall, the model predicts that more than 50% of all published research is probably wrong.

It should be noted that Dr Ioannidis's study suffers from its own particular bias. Important as medical science is, it is not the be-all and end-all of research. Other sciences, such as physics and chemistry, with more certain theoretical foundations and well-defined methods and endpoints, probably can do better than medicine. Still, he makes a good point—and one that lay readers of scientific results would do well to bear in mind.

With respect to analytical chemistry, researchers do have a better chance to create accurate results. Although the researcher has no means to know the truth for an unknown sample, he can often cross-check his methodology either with known samples (certified reference materials) or he can compare his results with different methods including definitive methods. It is EVISA's aim to encourage all researchers working in the field of speciation analysis, to make consistent use of available methodology to assure the quality of research results and to avoid poor experimental design. 

 Related Studies

 John P. A. Ioannidis, Why Most Published Research Findings Are False, PLoS Medicine, 2/8 (2005) e124.  doi:10.1371/journal.pmed.0020124

The PLoS Medicine Editors, Minimizing Mistakes and Embracing Uncertainty, PLoS Med 2(8) (2005) e272. doi:10.1371/journal.pmed.0020272

S. Goodman, S. Greenland, Why Most Published Research Findings Are False: Problems in the Analysis, PLoS Med.,  4/4 (2007) e168. doi:10.1371/journal.pmed.0040168

J.P.A. Ioannidis, Why Most Published Research Findings Are False: Author's Reply to Goodman and Greenland, PLoS Med 4/6 (2007) e215. doi:10.1371/journal.pmed.0040215

Related Publications

 Kevin A. Francesconi, Michael Sperling, Speciation analysis with HPLC-mass spectrometry: time to take stock, Analyst (London), 130/7 (2005) 998-1001. DOI: 10.1039/b504485p

 M. Valcárcel, A. Rios, Required and delivered analytical information: the need for consistency, Trends Anal. Chem. (Pers. Ed.), 19/10 (2000) 593-598. DOI: 10.1016/S0165-9936(00)000344-3

 Peter T. Kissinger, Jean-Michel Kauffmann, Quality manuscripts in analytical chemistry, Talanta, 57/3 (2002) 601-603. doi:10.1016/S0039-9140(02)00051-6

 Related EVISA Resources

Link page: All about quality of measurements
Brief summary: Error sources in speciation analysis
Brief summary: Speciation Analysis - Striving for Quality

Related News

Scientific American, Februar 27, 2007: The Science of Getting It Wrong: How to Deal with False Research Findings

last time modified: June 20, 2020


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