Zulfia Laws of Statistical Estimation: Φ-Force Over Information Curvature

Estimation fails not from variance, but from imbalance. For a century Fisher infor-mation I(θ) ruled statistical theory because curvature was computable, not because itwas fundamental.We introduce Zulfia Laws of Statistical Estimation: a theory built on arithmeticaddition rather than geometric curvature. The primitive decomposition is θ∗ = u + η +∆prior, where u is uncertainty, η is data force, and ∆prior is prior shift.Three laws follow directly:Law 1 - Consistency: KstatZ = |η|+|∆||η+∆| < 1 is necessary and sufficient for convergence.Law 2 - Φ-Force: F statΦ = Φ(ηdata + ∆prior) governs estimator behavior.Law 3 - Decomposition: Addition is primitive; curvature is derived.We prove Fisher information emerges as a measurement of Φ-Force under i.i.d andasymptotic conditions, not as a fundamental limit. Experiments demonstrate that Φ-Force predicts estimator failure where Fisher information declares optimality.Curvature measured the world for a century. Balance builds the next one.