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"Exposure to BPA may be harmful during pregnancy; this exposure may permanently affect the fetus," said Hugh S. Taylor, Ph.D., co-author of the study from Yale University School of Medicine in New Haven, Connecticut. "We need to better identify the effects of environmental contaminants on not just crude measures such as birth defects, but also their effect in causing more subtle developmental errors." Taylor and colleagues made this discovery by exposing fetal mice to BPA during pregnancy and examining gene expression and DNA in the uteruses of female fetuses. Results showed that BPA exposure permanently affected the uterus by decreasing regulation of gene expression. These epigenetic changes caused the mice to over-respond to estrogen throughout adulthood, long after the BPA exposure. This suggests that early exposure to BPA genetically "programmed" the uterus to be hyper-responsive to estrogen. Extreme estrogen sensitivity can lead to fertility problems, advanced puberty, altered mammary development and reproductive function, as well as a variety of hormone-related cancers. BPA has been widely used in plastics and other materials. Examples include use in water bottles, baby bottles, epoxy resins used to coat food cans, and dental sealants. "The BPA baby bottle scare may be only the tip of the iceberg." said Gerald Weissmann, M.D., Editor-in-Chief of The FASEB Journal. "Remember how diethylstilbestrol (DES) caused birth defects and cancers in young women whose mothers were given such hormones during pregnancy. We'd better watch out for BPA, which seems to carry similar epigenetic risks across the generations. "

Statistical validation of results, as Shaffer described it, simply involves testing the null hypothesis: that the pattern you detect in your data occurs at random. If you can reject the null hypothesis—and science and medicine have settled on rejecting it when there's only a five percent or less chance that it occurred at random—then you accept that your actual finding is significant. The problem now is that we're rapidly expanding our ability to do tests. Various speakers pointed to data sources as diverse as gene expression chips and the Sloan Digital Sky Survey, which provide tens of thousands of individual data points to analyze. At the same time, the growth of computing power has meant that we can ask many questions of these large data sets at once, and each one of these tests increases the prospects than an error will occur in a study; as Shaffer put it, "every decision increases your error prospects." She pointed out that dividing data into subgroups, which can often identify susceptible subpopulations, is also a decision, and increases the chances of a spurious error. Smaller populations are also more prone to random associations. In the end, Young noted, by the time you reach 61 tests, there's a 95 percent chance that you'll get a significant result at random. And, let's face it—researchers want to see a significant result, so there's a strong, unintentional bias towards trying different tests until something pops out. Young went on to describe a study, published in JAMA, that was a multiple testing train wreck: exposures to 275 chemicals were considered, 32 health outcomes were tracked, and 10 demographic variables were used as controls. That was about 8,800 different tests, and as many as 9 million ways of looking at the data once the demographics were considered.