Precision Illusion: A Framework for Studying Unsupported Numerical Specificity in AI

AI outputs can contain precise numbers that are not adequately supported by the available evidence—for example, citing “a 38.7% reduction” without a traceable source. An earlier exploratory program called this pattern the precision illusion.

That operationalisation is now retired rather than treated as an established measurement construct. Reproducibility and scientific-authority audits found that the archived work does not support a current prevalence, model-performance, or behavioral-effect claim.

Why does this matter? Research on the precision heuristic suggests that precise numbers can be perceived as more credible than less precise estimates, even when that precision is unwarranted. Unsupported numerical specificity may therefore influence reliance even when an answer is directionally plausible.

Our current work focuses on three questions:

1. Operationalization: How should unsupported numerical specificity be defined and measured? The prior UCS taxonomy is retained only as historical measurement-development material; no successor specification is frozen.
2. Measurement: How does it vary across domains, models, and interaction contexts? Protocol-valid prevalence estimates are not yet established.
3. Intervention: Can candidate outputs be revised without degrading appropriate content? CheckMyCoach is an engineering prototype for studying that workflow, not validated detection or repair evidence.

A behavioral experiment that would test whether unsupported numerical specificity affects trust and reliance was previously planned, but that Precision-Illusion behavioral thread was retired on 2026-08-18; no behavioral PI design is active, planned, or current. This post documents an earlier research direction.

Status note: This page records a historical exploratory program. Its operationalisation was retired after reproducibility and measurement audits; the behavioral experiment thread was retired 2026-08-18. A bounded measurement schema (M0 v1) was canonically frozen on 2026-08-19 as an instrument-testing asset; M0 execution is not activated and human data collection is not authorized.