The Statistical Bridge: From Events to Spines, Data Work, and the Interpretation of Results

This paper develops a Theory of Data architecture for the statistical bridge: the passage by which operational data become objects of formal statistical theory and by which formal results return as claims about the world. It distinguishes anchors from universes, realized event universes from virtual spine universes, recorded events from latent occurrence configurations, and occurrence from recording and member-value observation. A common anchor gives a place for a crossing but does not establish bridgeability: lawful attribution, a forward evidence-production account, a target relation, and assumptions adequate for the intended claim are also required. The paper treats data processing as analytical constitution rather than preliminary cleaning. Joins, restrictions, identity resolution, event assignment, reductions, and frame construction create populations, members, support, and model inputs. The same construction history governs interpretation. Five recurring failures organize the practical analysis: anchor substitution, universe substitution, hidden passage, an imaginary generative connection, and return overreach. A running customer-revenue case distinguishes deterministic event-to-spine constitution from statistical inference and shows how several valid analyses can arise from the same operational sources. The paper concludes with a provisional discipline for governing the inward and outward passages of statistical work and proposes an empirical evaluation program. It does not replace sampling theory, missing-data theory, point-process theory, regression, causal inference, or domain expertise. It supplies a common data foundation through which their local bridges can be built, reviewed, and interpreted.