Section 016 · Chapter 3, Sampling and Uncertainty
How Many Samples Are Enough?
Sample size is a risk decision. The higher the stakes and the rarer the failure, the more evidence builders need.
sample sizemany samples enough
What to do
- Start by asking what mistake would be expensive: shipping a bad system, blocking a good one, missing a rare severe failure, or spending too much time measuring tiny differences that do not matter.
- Define runnable checks that exercise sample size and many samples enough.
- Set acceptable outcomes and blocker failures for sample size and many samples enough before running the evaluation.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for sample size, many samples enough needed to reproduce work on How Many Samples Are Enough?.
- Report results for sample size, many samples enough by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
In production work, sample size depends on desired precision, expected failure rate, confidence level, and minimum detectable effect. Rare failures need targeted hunting because random sampling can require impractically large counts to observe very low-frequency events.
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Cite this page
Jason Arbon. "How Many Samples Are Enough?." Testing AI Knowledge Edition, section 16.
https://jarbon.ai/testing-ai/knowledge/ch016-many-samples-enough.html