Applying posterior probability informed thresholds to traditional cranial trait sex estimation methods.

Sex estimation methods using traditional cranial nonmetric traits utilize predictive models to produce a final sex estimation, using the resulting model's score to classify the individual. When sex estimations are assigned from discriminant scoring alone, statistical confidence in the resultant estimate is not always assessed or reported. Although some forensic anthropologists may qualitatively report their confidence in the assessment (e.g., "probable male"), these statements are subjective, not standardized, and not necessarily based on statistical results in a uniform way. The goals of this study were to evaluate how posterior probability-informed thresholds (PPITs) impacted accuracy rates, assess the balance between sample inclusion and accuracy for the proposed PPIT approach, and make recommendations for the use and interpretation of specific thresholds in casework. Using a sample of U.S. Black and White females and males (n = 292), we examined how PPITs can standardize the decision-making process of inferring sex for two methods using nonmetric cranial traits. We found that using PPITs of at least 0.85 increased accuracy (over 92% for some PPITs) yet remained highly inclusive of the sample. PPITs < 0.75 did not produce classification accuracy rates significantly higher than chance, and when using these cranial trait sex estimation methods, cases with posterior probabilities (PPs) <0.75 should be reported as "indeterminate." The 0.75-0.84 PPIT interval had an accuracy rate of 76%, which was both statistically significantly different from chance as well as from the higher (>0.85) groups, suggesting that although sex estimation at this level may be acceptable, the results hold lower confidence.