✅ AI Evaluation Sample Size Calculator
Estimate the number of AI evaluation examples needed for a target confidence level, margin of error, expected pass rate, and finite test population.
Calculate AI Evaluation Sample Size
What this AI evaluation sample size calculator calculates
Estimate how many representative test cases are needed to measure a binary pass rate within a chosen margin of error.
Statistical sample size does not fix poor test design. Cover important segments, failure modes, safety risks, languages, and task complexity.
AI Evaluation Sample Size Calculator formula
Assumptions and limitations
The result is an engineering estimate based on the values entered. AI models, tokenizers, runtimes, accelerators, cloud services, and provider billing rules differ. Validate important decisions with measured data from the exact model, hardware, framework, and pricing plan you intend to use.
Worked example
At 95% confidence, 50% expected pass rate, and ±5% margin, the conservative sample size is about 385 for a large population.
How to use the result
Statistical sample size does not fix poor test design. Cover important segments, failure modes, safety risks, languages, and task complexity.
- Start with representative production assumptions rather than best-case demos.
- Run a low, expected, and high scenario to understand the range.
- Record model version, pricing date, hardware, precision, and workload details.
- Replace assumptions with observed p50 and p95 measurements after testing.
Common mistakes to avoid
- Using a large sample of unrepresentative easy examples.
- Reporting overall accuracy while hiding critical segment failures.
- Treating correlated or duplicate examples as independent observations.
Frequently asked questions
Why is 50% expected pass rate conservative?
For a proportion, p=0.5 produces the largest variance and therefore the largest sample size.
Do I need this many examples per segment?
For independently precise segment metrics, each segment may need its own sample. The calculator shows a simple multiplied planning total.
Does this apply to pairwise preference tests?
The basic proportion formula can approximate a win rate, but power calculations for small model differences may require a dedicated paired-test design.
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Methodology and privacy
This educational tool uses the formula and assumptions displayed on the page. Calculations run locally in your browser, and the page does not transmit the values you enter. Results are estimates rather than provider quotes, benchmark guarantees, financial advice, or capacity guarantees. Last methodology review: August 2, 2026.
Formula Explorer connections
Interpretation: This relationship turns dataset size, batch structure or parallel capacity into a training and evaluation planning quantity. Assumption: Real results depend on data quality, hardware utilization, communication overhead, convergence behavior and the exact experimental design.