The Hudson–Kreitman–Aguade (HKA) test is based on the prediction from the neutral theory that levels of polymorphism within a species and the divergence between two closely related species should be correlated. Population subdivision has been shown to alter both the amounts of polymorphism segregating within species and the rate of divergence between species, meaning that genomic regions with different population structures also differ in their divergence to polymorphism ratios. Population subdivision may hence hamper the utility of the HKA test for detecting deviations from the standard neutral model, especially for organelle genomes that often have different patterns of population structure compared with nuclear genes. In this paper, I show that population subdivision inflates the number of instances where the HKA test detects deviations from the neutral model. Using coalescent simulations I show that this bias is most apparent when population subdivision is strong and differs substantially between the loci included. However, if divergence time is large and population structure substantial even changes in the levels of polymorphism and divergence associated with differences in the effective population size between two loci is enough to substantially alter the number of significant outcomes of the HKA test. A dataset on cytoplasmic diversity in Silene vulgaris and S. latifolia (Ingvarsson & Taylor, 2002) is also reanalysed. The previous study had shown a marked excess of intraspecific polymorphism in both species. However, when effects of population subdivision were removed, ad hoc, levels of intraspecific polymorphism were no longer significantly different from neutral expectations, suggesting that population subdivision contributed to the observed excess of intraspecific polymorphism seen in both species of Silene.