Outcome variance — the degree to which therapeutic results differ across individuals, programs, and measurement occasions — occupies a recurring methodological and interpretive position across the depth-psychology and behavioural-intervention corpus. The literature does not treat outcome variance as a nuisance to be eliminated but, rather, as a signal demanding explanation: heterogeneity statistics (I², Q-values) in meta-analytic work by Bettmann and Bowen reveal that the true variance among effect sizes substantially exceeds what sampling error alone can account for, compelling the search for moderators. Russell’s programme-evaluation studies frame variance questions developmentally, asking how outcomes differ by age, gender, diagnosis, and programme length. Benda’s structural-equation analyses partition unique variance attributable to religiousness, spirituality, and social attachment, distinguishing direct from indirect pathways to substance-use outcomes. DeMille employs regression modelling to isolate the fraction of post-treatment variance explicable by treatment participation versus intake severity. Across these traditions, a central tension persists: whether residual, unexplained outcome variance reflects unmeasured individual difference variables (readiness to change, attachment style, coping resources) or structural programme features (duration, therapeutic mode, staffing). The practical stakes are considerable — understanding what drives outcome variance informs both programme design and the equitable allocation of intensive interventions.