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Chi-Square Goodness-of-Fit Test API
x402
Chi-Square Goodness-of-Fit Test API is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-11).
Computes a chi-square goodness-of-fit test comparing observed frequency counts to expected frequency counts
Price / call$0.002
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object with a 'result' field indicating the test outcome and a 'status' field confirming whether the computation was successful. Likely includes the chi-square statistic and possibly a p-value or pass/fail determination on the distribution fit.
Example use cases
Fairness test for a dice game
“I want to check if my six-sided die is fair — I rolled it 120 times and got these observed counts per face: [18, 22, 19, 21, 20, 20]. The expected count for each face is 20. Can you run a chi-square goodness-of-fit test on that?”
Survey response distribution check
“I ran a survey with 4 answer options and expected an even split of 50 responses each. The actual responses were [62, 41, 55, 42]. Can you do a chi-square test to see if the distribution matches what I expected?”
A/B test category distribution validation
“I'm checking whether traffic across 5 campaign categories matches our projected distribution. Expected values are [100, 150, 200, 100, 50] and observed values are [110, 140, 195, 105, 50] — please run a chi-square goodness-of-fit test.”
Technical details
Endpoint
POSThttps://kihustle.tech/agents/api/v1/chi-square-goodness?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a quick, paid statistical computation of a chi-square goodness-of-fit test without setting up a local stats library. Suitable for lightweight agent workflows that need on-demand hypothesis testing against categorical frequency data. Prefer local computation (scipy, R) for large datasets or when chaining with other statistical tests.
Side effects
- Mismatched array lengths between expected and observed arrays — likely returns an error
- Zero values in expected array causing division-by-zero in chi-square computation
- Empty arrays provided — likely returns a validation error
- Non-numeric values in the arrays causing parse failures
- Sum of observed frequencies not matching sum of expected frequencies — may produce misleading results
- Network timeout or service unavailability
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| expected | array | |
| observed | array |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "processed",
"status": "success"
}
}Tags
x402
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