This work revisits angular calibration based on circular subdivision and presents a data-oriented interpretation within a digital metrology context. The approach does not modify the experimental procedure, but introduces calibration as an inverse parameter estimation problem using redundant observations and least-squares adjustment. Experimental datasets are used to illustrate the identification of systematic effects and the interpretation of measurement uncertainty in a task-specific manner. The results indicate that typical calibration data can be evaluated within a model-based framework, highlighting the roles of redundancy, data structure, and inference in dimensional metrology.