---
name: tai-ch014-chi-squared-tests-for-categorical-ai-quality
description: 'Apply chapter 14 of Testing AI, Chi-Squared Tests for Categorical AI Quality, 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 chi-squared tests for categorical ai quality.'
---

# Chi-Squared Tests for Categorical AI Quality

Skill name: `tai-ch014-chi-squared-tests-for-categorical-ai-quality`

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

## Purpose

Chi-squared tests help compare counts, categories, and failure distributions when quality is not
a smooth 0-10 score.

## 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

Use chi-squared tests when the outcome is categorical: pass or fail, safe or unsafe, grounded or
hallucinated, answer or refuse, correct tool or wrong tool, satisfied or escalated.

## 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.

## Deeper Guidance

For high-confidence decisions, check whether the test assumptions fit the data. Chi-squared
tests expect independent observations and enough expected count in each cell. If expected counts
are tiny, use Fisher's exact test or combine categories carefully before testing.