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Saylor Innovations A/B Test Significance Calculator
x402
Saylor Innovations A/B Test Significance Calculator is a paid API for AI agents from saylorinnovations.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).
Calculates A/B test statistical significance including lift, z-score, p-value, confidence interval, winner declaration, and required sample size per variant
Price / call$0.01
Success—
StatusUnknown
How this service works
A/B test significance from visitors and conversions - lift, z-score, p-value, confidence interval, winner and the sample size needed per variant
Output
Returns a JSON object with variant_a and variant_b objects (each containing visitors, conversions, and conversion_rate_percent), z_score, p_value, significant flag, winner (null if not significant), alpha threshold, lift, confidence interval, a note with context for interpreting the result, and actionable advice text.
Example use cases
Landing page test evaluation
“I ran a landing page A/B test — the control got 3500 visitors and 140 conversions, the new design got 3500 visitors and 196 conversions. Is the difference statistically significant and which version wins?”
Email campaign conversion analysis
“My email campaign split test is done: version A sent to 2000 people got 60 clicks, version B sent to 2000 people got 88 clicks. Can you run the significance test and tell me the p-value, z-score, and whether I have a clear winner?”
Sample size planning before launch
“I'm planning an A/B test for my checkout button and my baseline conversion rate is about 3.5%. How many visitors per variant do I need to detect a meaningful lift?”
Technical details
Endpoint
GEThttps://saylorinnovations.com/api/math/ab-test?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, hosted statistical significance calculation for a two-variant (A/B) conversion experiment without running your own stats library. Ideal for agents that need to interpret experiment results on the fly, report significance to stakeholders, or determine required sample sizes before launching a test. Best when inputs are simple visitor/conversion counts for two variants.
Side effects
- Missing required query parameters (a_visitors, a_conversions, b_visitors, b_conversions) returns validation error
- Non-numeric or zero visitor counts may cause division errors or unexpected results
- Extremely small sample sizes may produce unreliable statistical outputs
- Payment failure (402) if USDC balance is insufficient
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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