The study found that Binary Bayesian classifiers perform best with ethnic groups which show obvious differences in physical features. Additional research is needed in order to further verify these findings by studying groups not considered in the current study. Given the inevitable uncertainty of EIs, the authors recommend considering validated performance metrics while incorporating a particular EI into a wider body of evidence. The study used 15 autonomic STR loci in applying a binary Bayesian approach that involved computer-simulated and real STR profiles of Han Chinese, Japanese, Korean, American Caucasian, and a South Asian Indian aboriginal tribal group called Lodha. A correct classification rate of approximately 90 percent was achieved via simulation for each East Asian group (Han Chinese, Japanese, and Korean) when paired with the American Caucasian or the Tribal Indian group. A similar result was obtained between the Caucasian and the Indian group, but the correct classification rates among the three East Asian groups were much lower (approximately 70 percent). Results based on real profiles closely matched those achieved via simulation, thus establishing the credibility of the classifier performance metrics. The descriptions of methods address the compilation of the reference allele frequency database, Bayesian classification, validation via simulation, classification via simulation, and classification of real STR profiles. 1 figure, 1 table, and 24 references