Justin’s Notes

Correlation Across Many Variables

The idea that traits weakly distinguishing groups on any single measure become clearly diagnostic when many correlated measures are considered together.

It is clear that our perception of relatively large differences between human races and subgroups, as compared to the variation within these groups, is indeed a biased perception and that, based on randomly chosen genetic differences, human races and populations are remarkably similar to each other, with the largest part by far of human variation being accounted for by the differences between individuals. Human racial classification is of no social value and is positively destructive of social and human relations. Since such racial classification is now seen to be of virtually no genetic or taxonomic significance either, no justification can be offered for its continuance.
— Lewontin

In brief, Lewontin ignored the fact that differences among human populations are correlated. Although population variation at any one genetic locus tends to be small, global population structure becomes clear if one examines correlated differences across loci. For a simple analogy, consider men and women’s faces. If one takes any particular characteristic, say nose size, there is likely to be more variation within a sex than between the sexes (and it would be nearly impossible to classify faces by sex with any accuracy using only nose size). However, if one considers all facial characteristics together (which is, after all, how we actually experience human faces), then sex differences become sufficiently clear that an observer can guess the correct sex more than 95 percent of the time.

— Bo Winegard, Noah Carl Superior: The Return of Race Science—A Review