Introduction

Our review of Nei's $G_{st}$ and Weir and Cockerham's $\theta$ illustrated two important principles:

1. It's essential to distinguish parameters from estimates. Parameters are the things we're really interested in, but since we always have to make inferences about the things we're really interested in from limited data, we have to rely on estimates of those parameters.

2. This means that we have to identify the possible sources of sampling error in our estimates and to find ways of accounting for them. In the particular case of Wright's $F$-statistics we saw that, there are two sources of sampling error: the error associated with sampling only some individuals from a larger universe of individuals within populations (statistical sampling) and the error associated with sampling only some populations from a larger universe of populations (genetic sampling).¹

It shouldn't come as any surprise that there is a Bayesian way to do what I've just described. As I hope to convince you, there are some real advantages associated with doing so.