One size fits none: A call for an idionomic revolution

Clinical psychology is stalled. Treatment effect sizes have plateaued, syndromal diagnosis has not yielded actionable mechanisms, reliance on WEIRD-centric research limits cultural relevance, and rising mental-health needs continue to outstrip available care. New therapeutic brands keep appearing, yet few new principles or processes emerge, and practitioners still lack scientific guidance for fitting interventions to particular people's lives. These problems have many causes. One is deep and correctable: the ergodic error. Psychologists routinely apply average findings from ensemble-level statistics to particular people without verifying the assumptions that make such applications valid. This error is not incidental to clinical science's history. Population-ranking methods were originally developed for eugenic aims; clinical science retained their logic while abandoning their aims, yielding an analytic system poorly suited to the applied task: predicting and influencing what particular people do, in a particular context, over time. To move beyond this impasse, we propose idionomic analysis, which begins with idiographic modeling of change processes in particular people (e.g., individuals, couples, families), then derives functional groupings and nomothetic principles, retaining only those that deepen idiographic understanding. We discuss supporting algorithms: i-ARIMAX, iBoruta/tsBoruta, GIMME/S-GIMME, GSOM, Multilevel-VAR, idionomic random-effects meta-analysis, and PECAN/PLAN. Matching analytic methods to the demands of intervention is not a technical adjustment. It changes what applied life science can coherently claim, what will replicate, and whether personalized care is built from evidence about the clients being treated, or from averages that may describe no one.