# Selection Bias **Entity class:** Uncertainty And Evidentiary Layer concept **Selection Bias** is an evidentiary control for representing limits, error, bias, or confidence rather than silently converting inference into fact. Used correctly, Selection Bias supports conditional inference and collection planning without promoting proximity, exposure, or model output into proof. ## Counterterrorism predictive-graph role Its controls are part of the computational trust fabric, not peripheral paperwork: permissions, provenance, purpose, and auditability must remain machine-enforceable across systems. The analytic state should distinguish ground truth, observed evidence, inferred state, and predicted state. ## Relationships - **domain router:** [[wiki/Uncertainty Quantification|Uncertainty Quantification]]. - **ontology neighbors:** [[wiki/Spurious Correlation|Spurious Correlation]] and [[wiki/Sampling Bias|Sampling Bias]]. - **synthesis:** [[wiki/Counterterrorism Predictive Graph|Counterterrorism Predictive Graph]] and [[wiki/Predictive Intelligence Loop|Predictive Intelligence Loop]]. ## Sources - [NIST/SEMATECH — e-Handbook of Statistical Methods (accessed September 23, 2026)](https://www.itl.nist.gov/div898/handbook/) - [Oden Institute — Tan Bui-Thanh research profile (accessed September 23, 2026)](https://oden.utexas.edu/people/directory/Tan-Bui/) **As of:** 2026-09-23