[Good 2009 article by Andrew Gelman, in American Scientist, about some of the ways that statistics can go badly wrong. -egg]

Realistically, a researcher on sex ratios has to make two arguments: a statistical case that observed patterns represent real population effects and cannot be explained simply by sampling variability, and a biological argument that effects on the order of 1 percent are substantively important. The claimed effect size of 26 percent should have aroused suspicion in comparison to the literature on human sex ratios; in addition, though, the papers managed to survive the review process because reviewers did not recognize that the power of the studies was such that only very large estimated effects could make it through the statistical-significance filter. The result is essentially a machine for producing exaggerated claims, which of course only become more exaggerated when they hit the credulous news media (with an estimate of 4.7 percent ± 4.3 percent being ramped up to 26 percent and then reported as 36 percent).