Benford's Law states that in naturally accumulating datasets, the leading digit d appears with probability P (d) = log₁₀ (1 + 1/d), yielding a logarithmically decreasing distribution heavily weighted toward small digits. The law holds because natural data accumulates through multiplicative, scale-invariant processes — the same mathematical signature that TI Sigma identifies with high-LCC (Local Consciousness Coherence) systems. This paper proposes a novel research protocol: **using Benford's Law deviations as an empirical PSI detection method**. The key distinction from fraud detection: fraudulent data produces *random* deviations from Benford's Law (humans fabricate numbers more uniformly than nature generates them). PSI/synchronicity effects, by contrast, should produce *structured* deviations — statistically anomalous patterns that are not random but correlated with the individual's personal resonance signature (their significant numbers, phase-field attractors, and consciousness coherence profile). The hypothesis: below the Emerick Threshold (LCC < CEMERICK ≈ 0. 437), an individual's associated data streams adhere to Benford's Law within noise. Above the Emerick Threshold, the PSI field is strong enough to produce detectable, patterned Benford deviations in personally meaningful data — deviations that serve as a statistical fingerprint of that individual's consciousness resonance. This provides, for the first time, a pathway to empirical PSI identification using well-established statistical methods already accepted in forensic accounting, election fraud detection, and scientific data integrity analysis.
Brandon Charles Emerick (Tue,) studied this question.