Savic and Lindstrom (1) claim to have discovered significant differences in cerebral asymmetry and functional connectivity between homo- and heterosexual subjects. Unfortunately, the statistical analysis they present is strongly undermined by an inappropriate choice of variables. For example, in their analysis of cerebral asymmetry, the authors measure right and left cerebral volumes (R and L, respectively) and use these to construct an “asymmetry index” for each subject defined by V = (R − L)/(R + L) ≡ X/Y; they call this index AI. No details about the distribution of R and L are given in ref. 1, but if we assume that they are (correlated) bivariate normal variables then X = R − L and Y = R + L are in fact uncorrelated normal variables for which the exact ratio distribution is known (2) to have very long “tails.” This is a general problem with ratio distributions. Standard statistical methods, which assume normal distributions with rapidly decreasing tails, are not generally appropriate for this type of variable. Unfortunately, Savic and Lindstrom (1) seem unaware of this difficulty and proceed to use a standard t test and ANOVA: the P values emerging from this analysis are meaningless if the variables involved are ill conditioned.