MMP.14:2 - Problem

An optimizer can improve fit without repairing the needed prediction. A model can fail a broad diagnostic while retaining a sufficient narrower answer. Apparent disagreement can also come from comparing latent quantities with selected records, numerical approximation, or ordinary model-permitted variation.

Even a real discrepancy rarely names its cause. Large residuals might reflect a missing predictor, varying noise, dependence, selection or an erroneous observation. Wholesale replacement hides the changed assumption; making every observed feature look ordinary can fit chance patterns.

The working problem is to construct a comparison that can expose the relevant mismatch, trace it to a revisable part of the model, and establish what the revision changes without treating reused data or simulated agreement as new external evidence.