Sex—often analyzed through the visual assessment of robust or gracile nonmetric skeletal features—is one of the most important components of the biological profile, as it dictates the methods used in the estimation of age and stature. Differences in sexual dimorphism exist between populations; however, little work has tested the efficacy of nonmetric-scoring systems on Asian groups. This study examines cranial (nuchal crest, mastoid process, supraorbital margin, glabella, and mental eminence) nonmetric sexual dimorphism in 1,397 Japanese and Thai individuals through the development of population-specific univariate and multivariate statistics, which were subsequently tested on 109 Filipino individuals. The univariate sectioning points produced classification accuracies of 45.5–93.2%, while cross-validated logistic regression equations resulted in classification accuracies of 68.4–97.3%. However, Walker’s (2008) and Garvin et al.’s (2014) discriminant functions developed on non-Asian individuals produced lower classification accuracies and higher sex biases when applied to the Asian samples due to reduced sexual dimorphism. While some population differences exist between the Japanese and Thai, the inclusion of population in statistical models failed to contribute to the predictions. Further, the success of the univariate and multivariate models on the Filipino sample suggests that they can be cautiously applied to other East and Southeast Asian groups. While age and intraobserver error minimally affect the nonmetric traits, secular change may influence their expression, thereby complicating their use in sex estimation. The findings of this research underscore the importance of developing Asian-specific biological profile methods in the current judicial climate.

Methods for estimating assigned, binary sex at birth from skeletonized remains have primarily been developed for specific population groups in the U.S. (e.g., African American, European American, Hispanic) and, thus, inherently rely on ancestry estimation as a foundational component for constructing the biological profile. However, ongoing discussions in forensic anthropology highlight pressing issues with ancestry estimation practices. Therefore, this research provides population-inclusive assigned-sex estimation models for cases where ancestry is not estimated or is truly unknown. The study sample (n = 431) includes 3D volume-rendered skull computed tomography scans from the novel New Mexico Decedent Image Database of African, Asian, European, Latin, and Native Americans. Five standard nonmetric traits were scored, and eighteen standard measurements were obtained. Binary logistic regressions and discriminant function analyses were employed to produce models and classification accuracies, and intraobserver reliability was assessed. The population-inclusive nonmetric and metric models produced cross-validated classification accuracies of 81.0–87.0% and 86.7–87.0%, respectively, which did not differ significantly from the accuracy of most population-specific models. Moreover, combined nonmetric and metric models increased accuracy to 88.8–91.6%. This study indicates that population-inclusive assigned-sex estimation models can be used instead of population-specific models in cases where ancestry is intentionally not estimated, given current concerns with ancestry estimation.