Calculator
AI Evaluation Sample Size Calculator
Estimate the number of independent AI outputs to evaluate for a chosen confidence level and margin of error, with a simple evaluation budget.
Result
Approximate evaluation sample
385
Normal-approximation sample for independent representative binary outcomes.
Estimated evaluation spend
$57.75
Unadjusted sample size
384.16
How to use this calculator
What this calculator does
Estimate the number of independent AI outputs to evaluate for a chosen confidence level and margin of error, with a simple evaluation budget.
How to use it
Choose a confidence level, expected binary pass fraction and an absolute margin of error. Use a finite population only if the actual set of eligible outputs is known. Enter unit evaluation cost.
What the result means
This is a sample-size approximation for estimating one pass rate, not proof of reliability across subgroups or high-risk cases. Review sampling bias and outcome definitions separately.
Browse more ai calculators for related estimates and comparisons.
Actual AI costs depend on provider pricing, model choice, token usage, discounts, caching, retries, batching, tool calls, workflow design, and usage volume.
Scenario guidance
When to use this
Use to scope a representative pilot evaluation of a binary success metric.
Example scenario
A product team evaluates the fraction of correctly completed AI support tasks.
Inputs that usually matter most
- Confidence Z, anticipated pass rate and absolute margin
- Whether population is finite and samples independent
Common mistake to avoid
Do not interpret 385 tests as universal proof of system safety, or confuse margin of error with an error-rate target.
How to interpret the result
A rough statistical sample supports measurement; independently assess quality, coverage, risk and test selection.
Educational scenario with editable values; vendor billing rules, quality and service levels can differ.
Formula
Applies a normal approximation for a single independent binary outcome rate under approximately random representative sampling. Does not address stratification, correlated outputs, multi-class evaluation, drift, low expected counts or formal safety assurance.
Worked calculation
At 95% confidence, 50% expected pass rate and a five-percentage-point margin with a large population, the approximate required sample is 385 independent outputs. At $0.15 each, the evaluation cost is $57.75.
Frequently Asked Questions
Why use a 50% expected pass rate?
It is a conservative default for the normal approximation because variance is largest near 50%.
Can the result certify an AI system is safe?
No. A proportion estimate cannot replace adversarial testing, domain-specific evaluation or risk assessment.
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