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A/B Test Significance Calculator (Two-Proportion Z-Test)
x402
A/B Test Significance Calculator (Two-Proportion Z-Test) is a paid API for AI agents from api.agentstools.dev, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-14).
Computes A/B test statistical significance using a two-proportion z-test, returning observed lift, z-score, two-sided p-value, confidence interval on the lift, minimum detectable effect, and sample-size sufficiency.
Price / call$0.02
Success—
StatusUnknown
How this service works
A/B test significance with a two-proportion z-test: observed lift, z-score, two-sided p-value, confidence interval on the lift, minimum detectable effect, and sample-size sufficiency. Pure computation over your inputs.
Output
Returns a JSON object containing: the observed lift (percentage difference between variant and control conversion rates), the z-score, a two-sided p-value, a confidence interval (lower and upper bounds) on the lift, the minimum detectable effect given the sample sizes, and a boolean or flag indicating whether the sample size is sufficient to draw reliable conclusions.
Example use cases
Email subject line A/B test readout
“I ran an email campaign split test: version A was sent to 8,000 people and got 640 opens, version B went to 7,950 people and got 730 opens. Can you run the two-proportion z-test at 95% confidence and tell me if B's higher open rate is statistically significant and what the observed lift and p-value are?”
Landing page conversion experiment
“My landing page control had 12,400 visitors and 496 sign-ups, and the new variant had 11,800 visitors and 531 sign-ups. Check if that difference is statistically significant at 99% confidence, give me the z-score, p-value, and confidence interval on the lift, and tell me if my sample size is sufficient.”
Checking MDE before launching experiment
“I'm planning a pricing page test where my baseline conversion rate is about 3.2% and I expect roughly 4,000 visitors per arm over two weeks — what's the minimum detectable effect I can reliably measure at 95% confidence with that sample size?”
Technical details
Endpoint
POSThttps://api.agentstools.dev/marketing/experimentWhen to prefer
Choose this endpoint when you need a fast, pure-computation statistical significance check for a two-group proportion experiment (e.g. conversion rates, click-through rates) without setting up a local stats library. It is ideal for AI agents that need to interpret A/B test results on the fly, check sample-size sufficiency before declaring a winner, or compute MDE for experiment planning — all with no side effects and deterministic results from your inputs alone.
Side effects
- Missing or zero sample sizes cause division errors or invalid z-score computation
- Conversion counts exceeding sample size return a validation error
- Extremely small sample sizes may produce unreliable MDE or power estimates
- Invalid confidence level values (outside 0–1 or not a recognized percentage) return an error
- Network timeout or service unavailability returns a 5xx error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| a | object | Control arm with conversions and visitors |
| b | object | Variant arm with conversions and visitors |
| alpha | number | Significance level, default 0.05 |
| power | number | Target power for sample-size math, default 0.8 |
Tags
x402
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