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the history of human biology as an application of Principal Component Analysis

A tale from the history of human biology brings out the point. John Arbuthnot, an eighteenth-century British physician, noted a fact that greatly surprised him. Studying the registry of births in London between 1629 and 1710, he found that all of the years he reviewed showed a preponderance of male births: in his terms, each year was a “male year.” If you were a mad devotee of mechanistic analysis, you might think of explaining this—“in principle”—by tracing the motions of individual cells, first sperm and eggs, then parts of growing embryos, and showing how the maleness of each year was produced. But there is a better explanation, one that shows the record to be no accident. Evolutionary theory predicts that for many, but not all, species, the equilibrium sex-ratio will be 1:1 at sexual maturity. If it deviates, natural selection will favor the underrepresented sex: if boys are less common, invest in sons and you are likely to have more grandchildren. This means that if one sex is more likely to die before reaching reproductive age, more of that sex will have to be produced to start with. Since human males are the weaker sex—that is, they are more likely to die between birth and puberty—reproduction is biased in their favor.

The idea of a “theory of everything” is an absurd fantasy. Successful sciences are collections of models of different types of phenomena within their domains. The lucky ones can generate models that meet three desiderata: they are general, they are precise, they are accurate. Lots of sciences, natural sciences, are not so fortunate. As the ecologist Richard Levins pointed out decades ago, in many areas of biology—and, he might have added, in parts of physics, chemistry, and earth and atmospheric science as well—the good news is that you can satisfy any two of these desiderata, but at the cost of sacrificing the third. Contemporary climatology often settles for generality and accuracy without precision; ecologists focusing on particular species provide precise and accurate models that prove hard to generalize; and of course if you abandon accuracy, precision and generality are no problem at all.