---
name: tai-ch009-basic-stats-every-ai-builder-should-know
description: 'Apply chapter 9 of Testing AI, Basic Stats Every AI Builder Should Know, 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 basic stats every ai builder should know.'
---

# Basic Stats Every AI Builder Should Know

Skill name: `tai-ch009-basic-stats-every-ai-builder-should-know`

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

## Purpose

A few practical statistics can help developers explain non-deterministic quality without
pretending the data is more precise than it is.

## 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 few basic statistics make non-deterministic quality visible. Mean, median, percentiles,
standard deviation, failure rate, and minimum score each answer a different quality question.
For example, the mean tells the overall level, the median tells the typical case, percentiles
show the tail, and the minimum tells whether something truly bad happened.

## 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

Expert reports avoid letting one metric dominate. For skewed distributions, the median and
percentiles may explain user experience better than the mean. For safety-sensitive systems, the
tail and failure rate often matter more than average quality.