Section 019 · Chapter 3, Sampling and Uncertainty
AI-Reported Confidence vs. Statistical Confidence
An LLM saying it is confident is not the same as a confidence interval calculated from sample data.
confidence intervalstatistical confidenceRAGreported confidence statistical confidence
What to do
- Treat them differently, report them differently, and never let model confidence pretend to be proof.
- Choose thresholds on held-out data using the cost of mistakes.
- Report quality together with coverage , the fraction of cases the system chose to answer.
- Use it when the output and error event are defined precisely enough to calibrate.
- Do not publish one global calibration number without slice counts and freshness.
Evidence to preserve
- Report quality together with coverage , the fraction of cases the system chose to answer.
- Preserve the inputs, versions, configurations, raw outcomes, and results for confidence interval, statistical confidence, RAG, reported confidence statistical confidence needed to reproduce work on AI-Reported Confidence vs. Statistical Confidence.
- Report results for confidence interval, statistical confidence, RAG, reported confidence statistical confidence by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Expert teams calibrate AI confidence. They check whether high-confidence judge decisions actually agree with expert humans more often than low-confidence decisions. If not, the confidence label is not useful for routing or release decisions.
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Cite this page
Jason Arbon. "AI-Reported Confidence vs. Statistical Confidence." Testing AI Knowledge Edition, section 19.
https://jarbon.ai/testing-ai/knowledge/ch019-reported-confidence-statistical-confidence.html