<p>Estimating the Minimum Number of Individuals (MNI) in commingled skeletal assemblages is a core task in forensic and archaeological anthropology. Traditional methods, based on the frequency of lateralized elements, often suffer from underestimation due to fragmentation and morphological similarity. This study introduces a new MNI estimation method grounded in the principle of certain exclusion (with a 99% confidence interval), which focuses on detecting osteometric incompatibilities between elements rather than identifying matches. We developed linear regression models based on a large international dataset (<i>n</i> = 2,969 individuals) to generate 99% prediction intervals between long bone measurements. A recursive protocol, termed Allometric Research by Exclusion (RAE), was implemented to isolate bones that cannot belong to the same individual. The method was validated via bootstrap simulations using independent samples from the Milano and Pretoria Bone collections. Across all assemblages, the exclusion-based MNI estimates were consistently higher than those obtained by frequency-based methods, and more closely approximated the real number of individuals. This trend was especially clear in small to mid-sized assemblages, where precise MNI estimation is most critical. The exclusion method offers a statistically grounded, replicable, and conservative approach to MNI estimation. Its integration into forensic and archaeological workflows may enhance the accuracy and defensibility of population reconstruction in complex contexts.</p>