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Mann-Whitney U Test Proxy
x402
Mann-Whitney U Test Proxy is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-13).
Performs a Mann-Whitney U non-parametric statistical test comparing two independent numeric groups to determine if they come from the same distribution.
Price / call$0.002
Success—
StatusUnknown
How this service works
Kostenlose Guides, Solo-Playbooks und Artikel zu KI, Automation und Side Hustles — für Menschen, die mit echten Systemen online Einkommen aufbauen wollen. Transparent finanziert über faire Affiliate-Links.
Output
Returns a JSON object with a 'result' field indicating the outcome of the Mann-Whitney U computation and a 'status' field confirming success. Detailed U-statistic and p-value fields may be included depending on implementation.
Example use cases
A/B test conversion rate comparison
“I have conversion scores from two versions of my landing page — variant A is [3, 5, 4, 6, 3, 7] and variant B is [8, 9, 7, 10, 8, 11]. Can you run a Mann-Whitney U test to see if one variant significantly outperforms the other?”
Clinical trial outcome analysis
“Compare these two patient groups' pain scores for me using a non-parametric test: treatment group is [2, 3, 2, 1, 4] and control group is [6, 7, 5, 8, 6]. I need to know if the treatment made a real difference.”
Salary equity analysis between departments
“I want to check if salaries differ significantly between two departments without assuming normal distribution — department A salaries are [55000, 60000, 58000, 62000] and department B are [72000, 68000, 75000, 70000]. Run a Mann-Whitney U test on those.”
Technical details
Endpoint
POSThttps://kihustle.tech/nexus/api/v1/mann-whitney-u-proxy?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a quick, low-cost non-parametric comparison of two independent numeric groups without assuming normal distribution, and when you are already integrating x402 micropayment-enabled APIs. Prefer this over parametric t-tests when your data violates normality assumptions.
Side effects
- Empty arrays for group_a or group_b cause computation failure
- Non-numeric values in the arrays result in a processing error
- Missing required group fields return a validation error
- Very large arrays may cause timeout or unexpected results
- Ambiguous or minimal response schema means the exact error format is uncertain
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| group_a | array | |
| group_b | array |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "processed",
"status": "success"
}
}Tags
x402
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