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Netzhandwerker A/B Test Evaluator
x402
Netzhandwerker A/B Test Evaluator is a paid API for AI agents from tools.halowerk.com, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-12).
Analyzes a two-group conversion experiment using both frequentist (p-value, confidence interval, required sample size) and Bayesian (Beta-Binomial posteriors, probability variant wins, expected loss) methods in closed form.
Price / call$0.002
Success—
StatusUnknown
How this service works
Reads one conversion experiment two ways at once. Frequentist: two-proportion test, p value, confidence interval on the absolute lift, and the sample size the requested minimum detectable effect needs at the requested power. Bayesian: Beta-Binomial posteriors, probability the variant is better, expected loss either way, credible intervals - closed form, not sampled, so the numbers repeat exactly. looks_taken carries the adjusted threshold and the rate you actually ran at.
Output
Returns frequentist results (p-value, absolute lift confidence interval, required sample size for the specified MDE and power) and Bayesian results (probability variant beats control, expected loss for choosing either arm, credible intervals for both rates) computed in closed form. Also returns the adjusted significance threshold and actual false positive rate when multiple interim looks have been taken.
Example use cases
Validating a landing page redesign
“I ran a landing page test for two weeks — control got 8500 visitors with 680 sign-ups, variant got 8420 visitors with 760 sign-ups. Can you run both a frequentist and Bayesian analysis at 95% confidence and 80% power, looking for at least a 10% relative improvement?”
Interim analysis with multiple looks
“We've peeked at our checkout flow A/B test 3 times already. Current numbers: control 5000 visitors, 200 conversions; variant 5100 visitors, 230 conversions. Run the evaluation with looks_taken set to 3 so the threshold gets adjusted for peeking — I want the corrected p-value and Bayesian expected loss.”
Planning sample size before launch
“Before I start my email CTA experiment, I want to know how many visitors I'll need. My baseline conversion rate is about 4% and I want to detect a 15% relative lift with 90% power and 95% confidence — can you calculate the required sample size? Use 1000 visitors and 40 conversions as a placeholder for the control.”
Technical details
Endpoint
POSThttps://tools.halowerk.com/v1/abtest/evaluate?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a single call that returns both frequentist and Bayesian statistics for a binary conversion experiment without sampling variance — the closed-form Bayesian computation means results are deterministic and reproducible. Especially valuable when you have peeked at results multiple times and need the Pocock-style adjusted threshold via looks_taken. Prefer this over general statistics libraries when you need all key metrics (p-value, CI, sample size, posterior probability, expected loss, credible intervals) in one structured response for an agent workflow.
Side effects
- conversions exceed visitors for either group — returns validation error
- confidence_level or power outside allowed range (0.5–0.9999 / 0.5–0.999) — returns 422
- looks_taken above 100 — returns validation error
- minimum_detectable_effect_rel outside 0.001–5 range — returns error
- payment not provided or insufficient — returns 402 Payment Required
- zero visitors in control or variant — returns 422 minimum constraint error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| power | number | Probability of finding that effect if it is there. |
| control | object | Baseline group. conversions may not exceed visitors. |
| variant | object | Group under test. conversions may not exceed visitors. |
| prior_beta | number | Beta prior, failures. |
| alternative | string | two_sided asks whether the groups differ, greater whether the variant is above the control. |
| looks_taken | integer | How often the running test has been evaluated. Above 1 the threshold is adjusted and the real false positive rate is reported. |
| prior_alpha | number | Beta prior, successes. 1 with prior_beta 1 is uniform. |
| confidence_level | number | |
| minimum_detectable_effect_rel | number | Relative effect the test should be able to find, e.g. 0.1 for ten percent above the control rate. |
Tags
x402
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