This paper examines conventional statistical methods in differential personality diagnostics and presents an alternative approach to capture intra-individual intercorrelations. Traditional techniques, including Pearson correlation coefficients and z-scores, standardize individual deviations using group-level data but often fail to reflect subtle interactions among personality facets. This omission can result in incomplete diagnostic profiles, potentially overlooking clinically significant patterns. We describe the mathematical underpinnings of these methods and introduce a procedure that computes the product of z-scores as a proxy for intra-individual covariance. Although this approach offers enhanced insight into individual personality structures, it is not without limitations and does not represent a definitive solution. Implications for clinical practice, epistemology, and future research in precision psychology are discussed to support

more robust diagnostic evaluations. Overall, the study clearly highlights the pressing need to combine refined statistical methods with clinical insight for a more accurate understanding of personality, paving the way for further research.