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Marketing Experiment A/B Test Significance Calculator
x402
Marketing Experiment A/B Test Significance Calculator is a paid API for AI agents from payai.agentstools.dev, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-15).
Runs a two-proportion z-test on A/B experiment data to return observed lift, z-score, p-value, confidence interval, 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 the observed lift between control and variant conversion rates, the z-score from the two-proportion z-test, a two-sided p-value, a confidence interval on the lift, the minimum detectable effect given the sample sizes, and a boolean or verdict on whether the sample size is sufficient for the specified power level.
Example use cases
Landing page conversion rate test
“I ran a landing page experiment — control had 8,000 visitors and 640 conversions, variant had 8,100 visitors and 720 conversions. Is the lift statistically significant at the 95% confidence level with power 0.8?”
Email campaign click-through experiment
“My email A/B test is done: version A (control) got 12,500 sends and 875 clicks, version B (variant) got 12,300 sends and 1,010 clicks. Can you compute the z-score, p-value, and confidence interval on the lift, and tell me if the sample size was sufficient?”
Checkout flow variant decision
“We tested a new checkout flow — the old one had 5,500 visitors with 412 purchases, the new one had 5,450 visitors with 489 purchases. Run the two-proportion z-test at alpha 0.01 and tell me if we have enough evidence to roll it out.”
Technical details
Endpoint
POSThttps://payai.agentstools.dev/marketing/experimentWhen to prefer
Use this endpoint when you need fast, pure-computation statistical significance testing for a two-arm conversion-rate experiment without spinning up a data science environment. It is ideal for agents that need to make ship/no-ship decisions based on A/B test results, or for reporting pipelines that must interpret experiment outcomes automatically. Prefer it over general-purpose statistics libraries when you want a single API call that bundles lift, z-score, p-value, CI, MDE, and sample-size sufficiency in one response.
Side effects
- Missing or malformed 'a' or 'b' arm objects (conversions or visitors fields absent)
- Visitors count of zero causing division-by-zero
- Conversions exceeding visitors count (invalid input)
- Alpha or power values outside valid range (0,1)
- Extremely small sample sizes producing unreliable z-test approximations
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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