MMP.16:8 - Common Anti-Patterns and How to Avoid Them

Anti-pattern	What goes wrong	Repair
Maximize a difference before deriving the record	Saturation, selection or aggregation can erase it	Compare the obtainable records, as in :5.1
Demand disjoint supports for every useful test	Discards informative but uncertain observations	Construct the required error or decision comparison
Average away an unknown without a probability basis	Hides the assumption deciding which design looks best	Retain conditional cases or supply the probability law
Maximize information about every model parameter	Can favor learning that leaves the receiving question unchanged	Select the target or consequences that matter
Treat the best candidate as an adequate account	A closed comparison can select a poor explanation of the subject	Use a consequential mismatch to reopen the family
Require new data whenever models disagree	Spends effort even when the answer is already sufficient	Compare with the attainable use of current information
Interpret an adaptive sample as if it were fixed in advance	Can invalidate the claimed uncertainty or error property	Derive the law for the actual choices and stopping rule