---
name: tai-ch010-confidence-intervals-saying-about-like-a-professional
description: 'Apply chapter 10 of Testing AI, Confidence Intervals: Saying About Like a Professional, as a workflow for evaluating AI and non-deterministic systems. Use for test planning, eval design, quality review, release evidence, examples, or coaching related to confidence intervals: saying about like a professional.'
---

# Confidence Intervals: Saying About Like a Professional

Skill name: `tai-ch010-confidence-intervals-saying-about-like-a-professional`

Based on **Testing AI: Engineering Confidence in AI Systems** by **Jason Arbon**.

## Purpose

Confidence intervals help testers report estimates as ranges instead of pretending sample
results are exact truth.

## Use This Workflow

- Identify the AI behavior or release decision being evaluated.
- Define realistic cases, slices, unacceptable outcomes, and evidence needed for confidence.
- Choose measurements that match the risk: rubric scores, samples, intervals, traces, human review, deterministic checks, or production monitors.
- Report uncertainty, severe failures, and decision impact instead of only a pass/fail result.

## Key Guidance

A confidence interval is a way to say "about" with discipline. It reports an estimate as a
range, acknowledging that a sample is not the full truth. For example, saying pass rate is 92%
sounds exact. Saying the approximate 95% confidence interval is 85% to 96% tells the team how
much uncertainty remains.

## Apply The Approach

Create representative cases, score them with explicit criteria, review severe failures separately, report uncertainty, and connect the evidence to a concrete decision.

## Expert Notes

At expert level, choose interval methods that match the metric. A pass rate is a proportion and
may use Wilson or exact binomial intervals. An average score often uses a t-based or bootstrap
interval, especially when the score distribution is not normal.