Anomaly Investigation Loop

A form → test → eliminate → confirm loop — root-cause analysis as a search. The artifact is an investigation log; the feedback signal is the count of live candidate explanations, driven down toward a single cause that is confirmed, not merely consistent. Each iteration you test one candidate against the data and drop the ones the data refutes, narrowing the field until one survives.

The discipline this enforces: a cause is "root" only when it both survives an honest attempt to refute it and makes a positive prediction that checks out (e.g. "if this is the cause, removing it restores normal" — and it does). A story that merely could explain the anomaly is a hypothesis, not a finding.

When to use

Use this when an anomaly is already in hand — you know roughly what looks wrong and want the cause diagnosed by elimination against the data. Default to a broad initial slate of mutually distinguishable causes, then test the one that splits the field fastest; if the anomaly is vague, your first job is to make it precise (iteration 0). Not for open-ended exploration of a dataset with no anomaly to chase (use data-analysis), and not for verifying an external claim against the literature (use claim-verify).