MMP.12:8 - Common Anti-Patterns and How to Avoid Them

Failure	Repair
A unique penalized minimizer is reported as an identified subject value.	Show which data-indistinguishable alternatives the penalty selected among; retain the selection premise.
A smaller residual is treated as a better reconstruction.	Compare propagated error and regularization bias for the required target, as in :5.1.
An old penalty is retained after the noise or forward relation changes.	Recalculate its discrepancy, sensitivity and lost target detail under the changed condition.
A smooth or learned reconstruction is used for a feature its construction suppresses.	Change the restriction or return the feature as unresolved; the rate surge in :5.3 shows the relevant loss.
A solver’s convergence is offered as stability of recovery.	State the fixed computational problem solved and separately establish sensitivity or the noise-dependent limit.