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A/B Test Statistical Significance Calculator
x402
A/B Test Statistical Significance Calculator is a paid API for AI agents from math-stats.x402tookit.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-29).
Computes conversion rates, relative lift, two-proportion z-test p-value, confidence interval, and a significance verdict for an A/B experiment.
Price / call$0.02
Success—
StatusUnknown
How this service works
A/B conversion test: rates, relative lift, two-proportion z-test p-value, confidence interval and a significance verdict.
Output
Returns conversion rates for both groups, the absolute difference in percentage points, relative lift percentage, pooled rate, z-statistic, two-sided p-value, confidence interval (lower/upper bounds with level), a boolean significance flag, a human-readable verdict string, a summary, reasons list, whether there were enough conversions, and optional degraded/disclaimer fields.
Example use cases
Landing page conversion test
“My landing page A/B test just ended — control got 8,500 visitors and 255 conversions, the new design got 8,300 visitors and 299 conversions. Was the improvement statistically significant at the 0.05 level, and what's the relative lift?”
Email campaign subject line experiment
“I ran a subject line test on my email campaign: version A had 25,000 opens out of 200,000 sends, version B had 27,800 opens out of 200,000 sends. Can you check if that difference is statistically significant using a 0.01 alpha?”
Pricing page CTA button test
“We changed our CTA button color and want to know if it worked — the old button saw 450 conversions from 15,000 visitors, the new one got 510 conversions from 14,800 visitors. Run the two-proportion z-test and give me the p-value and confidence interval.”
Technical details
Endpoint
GEThttps://math-stats.x402tookit.com/ab-test?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a complete, ready-to-interpret A/B test report — not just a raw p-value — including relative lift, confidence intervals, and a plain-language verdict. It is ideal for product and marketing teams running two-group conversion experiments where a two-proportion z-test is the standard method. Prefer it over a generic statistics library when you want the verdict and summary text included in the response without post-processing.
Side effects
- Missing required parameters (visitorsA, conversionsA, visitorsB, conversionsB) returns a validation error
- Alpha outside 0.001–0.2 range may be rejected or defaulted
- Conversions exceeding visitor count produces invalid input error
- Very small sample sizes may return a result with enoughConversions=false and a degraded flag
- Zero visitors in either group causes division-by-zero handling with a degraded response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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