About half of children are boys and half are girls, but that doesn’t mean that every couple is equally likely to have a boy or a girl each time they conceive a child. And evidence suggests that indeed the probability of conceiving a girl varies per couple.

I will simplify things for this post and look at a hypothetical situation abstracting away the complications of biology. This post fills in the technical details of a thread I posted on Twitter this morning.

Suppose the probability *p* that a couple will have a baby girl has a some distribution centered at 0.5 and symmetric about that point. Then half of all births on the planet will be girls, but that doesn’t mean that a *particular* couple is equally likely to have a boy or a girl.

How could you tell the difference empirically? You couldn’t if every family had one child. But suppose you studied all families with four children, for example. You’d expect 1 in 16 such families to have all boys, and 1 in 16 families to have all girls. If the proportions are higher than that, and they are, then that suggests that the distribution on *p*, the probability of a couple having a girl, is not constant.

Suppose the probability of a couple having girls has a beta(*a*, *b*) distribution. We would expect *a* and *b* to be approximately equal, since about half of babies are girls, and we’d expect *a* and *b* to be large, i.e. for the distribution be fairly concentrated around 1/2. For example, here’s a plot with *a* = *b* = 100.

Then the probability distribution for the number of girls in a family of *n* children is given by a beta-binomial distribution with parameters *n*, *a*, and *b*. That is, the probability of *x* girls in a family of size *n* is given by

The mean of this distribution is *na*/(*a*+*b*). And so if *a* = *b* then the mean is *n*/2, half girls and half boys.

But the variance is more interesting. The variance is

The variance of a binomial, corresponding to a constant *p*, is *np*(1-*p*). In the equation above, *p* corresponds to *a*/(*a*+*b*), and (1-*p*) corresponds to *b*/(*a*+*b*). And there’s an extra term,

which is larger than 1 when *n* > 1. This says a beta binomial random variable always has more variance than the corresponding binomial distribution with the same mean.

Now suppose a family has had *n* children, with *g* girls and *n* – *g* boys. Then the posterior predictive probability of a girl on the next birth is

If *g* = *n*/2 then this probability is 1/2. But if *g* > *n*/2 then the probability is greater than 1/2. And the smaller *a* and *b* are, the more the probability exceeds 1/2.

The binomial model is the limit of the beta-binomial model as *a* and *b* go to infinity (proportionately). In the limit, the probability above equals *a*/(*a*+*b*), independent of *g* and *n*.

From what perspective? The generic ∼51.3% male bias [1], WHO’s male to female ratio (105 to 100) [2], lower male-survivability, birth location[3], or the season[4] (which should climb assuming climate change)?

[1] https colon //doi.org/10.1073/pnas.1505165112

[2] https colon //utswmed.org/medblog/it-boy-or-girl-fathers-family-might-provide-clue/

[3] https colon //ourworldindata.org/sex-ratio-at-birth

[4] https colon //doi.org/10.3390/ijerph110808166