# Marketing Experiment A/B Test Significance Calculator

> 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.

## Facts

- Endpoint: POST https://payai.agentstools.dev/marketing/experiment
- Price: $0.02/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/marketing-experiment-a-b-test-significance-calculator-f8cd133f
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Vs9asKyPZfOBp4CKai8SQ

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability marketing-experiment-a-b-test-significance-calculator-f8cd133f -d '<json body>'
```

Example prompt: Can you check if my A/B test is statistically significant? The control arm had 4,200 visitors with 310 conversions, and the variant had 4,150 visitors with 365 conversions — use alpha 0.05 and target power of 0.8.

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "a": {
   "type": "object",
   "description": "Control arm with conversions and visitors"
  },
  "b": {
   "type": "object",
   "description": "Variant arm with conversions and visitors"
  },
  "alpha": {
   "type": "number",
   "description": "Significance level, default 0.05"
  },
  "power": {
   "type": "number",
   "description": "Target power for sample-size math, default 0.8"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/marketing-experiment-a-b-test-significance-calculator-f8cd133f/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from payai.agentstools.dev](https://www.zero.xyz/host/payai.agentstools.dev/llms.txt)