KB article
Outliers and Null Semantics: When ‘Missing’ Means Something
Outliers and nulls can be meaningful; AI must interpret them correctly.
arf-kbanalytical-explainabilitynull-semanticsoutliersexplainability
TL;DR
- Missing data is not always zero.
- Outliers can distort explanations.
The problem
- Null values are treated as zeros or ignored.
- Outliers skew explanations and trends.
Why it matters
- Misinterpreting nulls leads to wrong conclusions.
- Outliers can hide the real story.
Symptoms
- AI says “no change” when data is missing.
- Explanations are driven by a few extreme points.
Root causes
- No null semantics documented.
- No outlier handling in measures.
What good looks like
- Null semantics defined (unknown vs not applicable).
- Outlier handling rules documented.
How to fix
- Document null meaning in metadata.
- Add measures that exclude or flag outliers.
- Explain when data is missing.
Pitfalls
- Treating null as zero by default.
- Silently excluding outliers.
Checklist
- Null meaning documented.
- Outlier handling implemented.
- AI responses mention missing data.
Framework placement
Primary ARF layer: Analytical Explainability. Diagnostic bridge: semantic-reliability, execution-reliability, change-reliability.