Statistical sex differences are like flipping two different collections of coins with different biases, where the coins represent various traits. Learning the outcome of any individual flip, doesn't tell you which set that coin came from, but [if we look at the aggregation of many flips, we can get _godlike_ confidence](https://www.lesswrong.com/posts/cu7YY7WdgJBs3DpmJ/the-univariate-fallacy-1) as to which collection we're looking at.
A single-variable measurement like height is like a single coin: unless the coin is _very_ biased, one flip can't tell you much about the bias. But there are lots of things about people for which it's not that they can't be measured, but that the measurements require _more than one number_—which correspondingly offer more information about the distribution generating them.
Statistical sex differences are like flipping two different collections of coins with different biases, where the coins represent various traits. Learning the outcome of any individual flip, doesn't tell you which set that coin came from, but [if we look at the aggregation of many flips, we can get _godlike_ confidence](https://www.lesswrong.com/posts/cu7YY7WdgJBs3DpmJ/the-univariate-fallacy-1) as to which collection we're looking at.
A single-variable measurement like height is like a single coin: unless the coin is _very_ biased, one flip can't tell you much about the bias. But there are lots of things about people for which it's not that they can't be measured, but that the measurements require _more than one number_—which correspondingly offer more information about the distribution generating them.