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Mann-Whitney U Test
x402
Mann-Whitney U Test is a paid API for AI agents from stats-tests.x402tookit.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-21).
Performs the Mann-Whitney U test on two independent samples, returning U statistics, exact or approximate p-values, and rank-biserial effect size.
Price / call$0.02
Success—
StatusUnknown
How this service works
Mann-Whitney U test for two independent samples: U, exact p for small samples without ties or normal approximation, effect size.
Output
Returns U statistics for both samples (uA, uB), rank sums, sample sizes, two-sided p-value, whether exact or normal approximation was used, rank-biserial correlation as effect size, probability that a random observation from A exceeds one from B, a significance verdict at the 5% level, a plain-language summary, and flags for ties or degraded results.
Example use cases
Clinical trial treatment comparison
“I have pain scores from a control group and a treatment group from my study — control: 6,8,7,9,5,8 and treatment: 3,4,5,2,4,3. Can you run a Mann-Whitney U test and tell me whether the treatment significantly reduced pain scores and what the effect size is?”
A/B test for non-normal user metrics
“Compare these session durations in seconds for two versions of my app — version A: 45,120,30,200,85,60 and version B: 90,150,110,180,130,95 — using a Mann-Whitney U test and tell me if version B is significantly higher.”
Educational intervention assessment
“I'm comparing pre-test scores for students who got tutoring vs those who didn't — tutored: 72,85,90,78,88 and untutored: 55,60,70,58,65 — can you do a Mann-Whitney U test and give me the p-value and effect size?”
Technical details
Endpoint
GEThttps://stats-tests.x402tookit.com/mann-whitney?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a non-parametric test for two independent samples — especially when data is ordinal, skewed, or cannot be assumed to follow a normal distribution. Prefer it over a t-test when normality is questionable or sample sizes are small. It is the right choice when you need both a significance test and a rank-based effect size (rank-biserial correlation) in one call.
Side effects
- Missing or unparseable sample values in 'a' or 'b' parameters
- Samples with fewer than 2 observations may produce unreliable results
- Ties present in data trigger normal approximation instead of exact p-value (flagged in response)
- Extremely large samples may fall back to normal approximation regardless
- Non-numeric values in sample strings cause parsing errors
- Empty sample strings return validation errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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