Statistics and Inference as Standing Preserving Bookkeeping

This paper reclassifies statistics and inference as standing-preserving bookkeeping practices rather than as epistemic engines that extract truth, likelihood, or belief from data. It shows that statistical inference operates by stabilizing reference across noisy, incomplete, or heterogeneous observations, not by licensing claims about underlying causes, probabilities, or degrees of belief. Within a closed admissibility framework, inferential constructs—such as estimators, confidence measures, priors, and update rules—function to maintain comparability and coherence under representational limitation. They do not generate new facts, validate hypotheses, or confer epistemic warrant beyond the preservation of admissible reference. When inference is treated as belief formation, evidence aggregation, or truth approximation, representational constraints are illicitly reinterpreted as epistemic authority. The analysis is eliminative. No alternative inferential methods are proposed, and no procedural guidance is offered for statistical practice. Instead, the paper establishes a boundary condition: inferential outputs are admissible only as scope-relative summaries that preserve standing across data representations, not as claims about what is likely, true, or real. By situating statistics and inference alongside other non-ontic bookkeeping regimes, the paper dissolves foundational disputes over Bayesianism, frequentism, and evidential justification. It closes these debates by showing that their disagreements arise from category errors about the role of inference, not from unresolved questions about knowledge or reality.