Tuesday, July 3, 2012

Scientific Integrity is an Oxymoron

"How can an otherwise sane individual become so enamored of a fantasy, an imposture, that even after it’s exposed in the bright light of day, he still clings to it – indeed, clings to it all the harder? No amount of logic can shatter a faith consciously based on a lie." ~ Lamar Keene, a scam artist who posed as a psychic, describing why it was so easy to fleece people.

Parkinson’s Researcher Fabricated Data - Neuroscientist Mona Thiruchelvam agrees to retract two studies linking neurodegeneration to pesticides.
By Hayley Dunning

Thiruchelvam fabricated stereological cell count data in two studies on how pesticides influence neuronal mechanisms involved in Parkinson’s disease (PD). The studies reported the results of 13 new experiments that supposedly counted nigrostriatal neurons in the brains of mice and rats, but an investigation spearheaded by the UMDNJ determined those counts were never taken. The nigrostriatal pathway is a major dopamine circuit in the brain, and loss of neurons in this area is one of the main features of Parkinson’s disease…… After the investigation was passed back to UMDNJ and the findings were confirmed, Thiruchelvam was notified and provided with an opportunity to respond. She never did, and in February 2010, she left the UMDNJ. The ORI sent her a Voluntary Exclusion Agreement, which excludes her from federal funding and serving on advisory committees for seven years, which she signed without comment.

Why Most Published Research Findings Are False

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.



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