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Numora Chi-Square Statistics Endpoint
x402
Numora Chi-Square Statistics Endpoint is a paid API for AI agents from numomo.vercel.app, paid per call via x402, $0.1/call, status unknown (last checked 2026-09-14).
Computes a chi-square goodness-of-fit or independence test given observed (and optionally expected) frequency arrays, returning the test statistic, p-value, and degrees of freedom.
Price / call$0.1
Success—
StatusUnknown
How this service works
100 pure math computation endpoints for AI agents. Statistics, financial math, linear algebra, equation solving, calculus, number theory, sequence generation, and unit conversions. Zero external dependencies. x402 micropayments on Base.
Output
Returns a JSON object with a boolean 'success' field, a human-readable 'computation' string describing what was calculated, and a 'result' object containing the chi-square test statistic, degrees of freedom, p-value, and potentially a significance conclusion.
Example use cases
Survey response distribution test
“I ran a customer satisfaction survey and got these response counts across five categories: [45, 30, 55, 20, 50]. The expected distribution should be equal across all five. Can you run a chi-square test to see if the responses are significantly different from what I'd expect by chance?”
A/B test categorical outcome analysis
“My A/B test returned these click-vs-no-click counts for variant A: [320, 680] and for variant B: [410, 590]. Wait, actually just check if my observed counts [320, 680] differ from the expected [365, 635] — run a chi-square test and give me the p-value.”
Genetics ratio hypothesis test
“I'm checking a Mendelian genetics experiment where I observed offspring counts of [78, 42, 36, 14] in four phenotype categories. The theoretical 9:3:3:1 ratio predicts [96.19, 32.06, 32.06, 10.69]. Can you do a chi-square goodness-of-fit test on these numbers?”
Technical details
Endpoint
POSThttps://numomo.vercel.app/api/statistics/chisquareWhen to prefer
Choose this endpoint when you need a fast, serverless, zero-dependency chi-square statistical test without setting up a Python/R environment or calling a heavyweight analytics platform. Ideal for AI agents that need on-the-fly hypothesis testing for categorical data, A/B test evaluation, or goodness-of-fit checks within a pipeline. The micropayment model via x402 on Base makes it suitable for pay-per-use agent workflows.
Side effects
- Missing 'observed' array returns a 400 validation error
- Observed and expected arrays of mismatched lengths may cause a computation error
- Expected frequencies containing zeros can cause division-by-zero issues
- Arrays with fewer than 2 categories may produce degenerate results
- Non-numeric values in arrays will likely return an error response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| expected | array | |
| observedrequired | array |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| resultrequired | object | Computation result varies by endpoint |
| successrequired | boolean | Always true on success |
| computationrequired | string | Human-readable description of what was computed |
Tags
x402
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