Section 024 · Chapter 4, Statistical Tests for AI Quality
Statistical Significance vs. Practical Significance
A difference can be statistically credible and still too small to matter. Confidence Engineers need to explain both sides.
confidence engineerstatistical significance practical significance
What to do
- Define runnable checks that exercise confidence engineer and statistical significance practical significance.
- Set acceptable outcomes and blocker failures for confidence engineer and statistical significance practical significance before running the evaluation.
- Run representative cases for confidence engineer and statistical significance practical significance and preserve the failures that would change the decision.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for confidence engineer, statistical significance practical significance needed to reproduce work on Statistical Significance vs. Practical Significance.
- Report results for confidence engineer, statistical significance practical significance by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
In production work, define the minimum meaningful effect before testing. If the team only cares about improvements of at least 0.3 points or a 20% reduction in policy failures, say so before looking at the data.
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Cite this page
Jason Arbon. "Statistical Significance vs. Practical Significance." Testing AI Knowledge Edition, section 24.
https://jarbon.ai/testing-ai/knowledge/ch024-statistical-significance-practical-significance.html