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A two-step procedure is presented for analysis of θ (FST) statistics obtained for a battery of loci, which eventually leads to a clustered structure of values. The first step uses a simple Bayesian model for drawing samples from posterior distributions of θ-parameters but without constructing Markov chains. This step assigns a weakly informative prior to allelic frequencies and does not make any assumptions about evolutionary models. The second step regards samples from these posterior distributions as “data” and fits a sequence of finite mixture models, with the aim of identifying clusters of θ-statistics. Hopefully, these would reflect different types of processes and would assist in interpreting results. Procedures are illustrated with hypothetical data, and with published allelic frequency data for Type-II diabetes in three human populations, and for 12 isozyme loci in 12 populations of the argan tree in Morocco.