Are bias and fraud damaging the existing public trust in scientific and medical research? (previously) [more inside]
posted by jeffburdges on May 13, 2012 - 35 comments

According to this substantial study recently published in Psychological Science, "lower general intelligence (g) in childhood predicts greater racism in adulthood, and this effect was largely mediated via conservative ideology.". As the Daily Mail summarises, right-wingers are less intelligent than left wingers. [more inside]
posted by wilful on Feb 6, 2012 - 119 comments

A Mismeasured Mismeaurement of Man. Stephen Jay Gould's classic The Mismeasure of Man argues that 19th century scientist Samuel George Morton inflicted his own racial biases on his data to demonstrate that Caucasians had larger brains than other races. A new paper in the Public Library of Science: Biology debunks Gould's account by remeasuring the same skulls Morton used. Whatever biases Morton may have had, they are not reflected in the data.
posted by Horace Rumpole on Jun 10, 2011 - 55 comments

Statistical hypothesis testing with a p-value of less than 0.05 is often used as a gold standard in science, and is required by peer reviewers and journals when stating results. Some statisticians argue that this indicates a cult of significance testing using a frequentist statistical framework that is counterintuitive and misunderstood by many scientists. Biostatisticians have argued that the (over)use of p-vaues come from "the mistaken idea that a single number can capture both the long-run outcomes of an experiment and the evidential meaning of a single result" and identify several other problems with significance testing. XKCD demonstrates how misunderstandings of the nature of the p-value, failure to adjust for multiple comparisons, and the file drawer problem result in likely spurious conclusions being published in the scientific literature and then being distorted further in the popular press. You can simulate a similar situation yourself. John Ioannidis uses problems with significance testing and other statistical concerns to argue, controversially, that "most published research findings are false." Will the use of Bayes factors replace classical hypothesis testing and p-values? Will something else?
posted by grouse on Apr 11, 2011 - 45 comments

In “Understanding Current Causes of Women’s Underrepresentation in Science,” Cornell professors Stephen Ceci and Wendy Williams provide a thorough analysis and discussion of 20 years of data. [more inside]
posted by Tanizaki on Feb 20, 2011 - 103 comments

Rigging a study to make conservatives look stupid.
posted by veedubya on Sep 22, 2007 - 56 comments