Aglet

Learn From Repeated Average Definition Errors

An average lesson preserves the uneven example that made an unweighted and weighted result diverge. Record the source fields, filter, denominator, label, and decision context. Then improve the metric dictionary or review practice so future measures expose their population and weighting rule before a comparison is published.

Keep the lesson for the next incident

  1. Retain the uneven case

    Keep rows with unequal group sizes, explicit weights, a filtered subset, and expected weighted and unweighted results. Record precision, exclusions, report labels, and export headers. Preserve the original calculation trace so a later reviewer can see why two valid averages answer different questions.

  2. Classify the missing contract

    Review prior disputes for hidden group averaging, changed denominators, weight fields used by convention, and labels that said average without naming the unit. Group cases by definition or review gap. Use observed fixtures and decisions instead of assigning an invented prevalence.

  3. Make metric review durable

    Assign the metric owner to maintain the weighting statement and the report owner to check it when joins, filters, or exports change. Add a trigger for new denominator rules. State which zero, missing, and negative-weight cases the fixture covers and which require a policy decision.

What to carry forward

The learning record is complete when it links average confusion to a missing unit, weight, or denominator contract, preserves an uneven fixture, and names an owner and trigger. Keep paired measures explicit when both are useful rather than collapsing them into one ambiguous label.

Technical background: PostgreSQL documentation.

Keep the decision with the work.

Use a Work Item in Aglet to record the problem, the evidence you have, and the next decision. Add an owner and priority, then keep updates in the discussion so the next person can follow the reasoning.

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