---
name: tai-ch014-statistical-significance-vs-practical-significance
description: 'Apply chapter 14 of Testing AI, Statistical Significance vs. Practical Significance, 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 statistical significance vs. practical significance.'
---

# Statistical Significance vs. Practical Significance

Skill name: `tai-ch014-statistical-significance-vs-practical-significance`

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

## Purpose

A difference can be statistically credible and still too small to matter. Testers need to
explain both sides.

## 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

Statistical significance asks whether a difference is likely to be more than random noise.
Practical significance asks whether the difference matters enough to change a product decision.
For example, a huge sample can make a tiny improvement statistically significant, while users
may never notice the change.

## 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, 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.