calibration-noise-distribution-invariance
IN premise — summaries/2026/08/24/aristotelian-2026-sR-references-chunk-2.md
Created 2026-08-24T17:10:51+00:00
Permutation-based null calibration correctly collapses scores to zero under Gaussian, Student-t, Laplace, and Gaussian mixture noise without requiring any distributional assumptions about the data-generating process
Summary
A specific statistical calibration check (permutation-based null calibration) gives the correct "nothing special is going on" result no matter what shape the background noise has — whether it's smooth, heavy-tailed, or lumpy. This means the system can rely on this test as a trustworthy baseline without first having to identify or assume the noise model, making it safe to apply broadly across different data sources.