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KiHustle Chi-Square Goodness-of-Fit Test
x402
KiHustle Chi-Square Goodness-of-Fit Test is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-14).
Performs a chi-square goodness-of-fit statistical test comparing observed frequencies against expected frequencies
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 chi-square computation outcome and a 'status' field confirming success. The response indicates whether the test was processed successfully, though detailed chi-square statistic, p-value, and degrees-of-freedom breakdown may vary by implementation.
Example use cases
Dice fairness test
“I rolled a die 120 times and got these counts per face: [18, 22, 19, 21, 20, 20]. Can you run a chi-square goodness-of-fit test against the expected uniform distribution of 20 per face to check if the die is fair?”
Survey response distribution check
“My survey had expected response proportions of [0.4, 0.3, 0.2, 0.1] across four categories, and I got observed counts of [45, 28, 18, 9] from 100 respondents. Run a chi-square test to see if the responses match the expected distribution.”
A/B traffic split validation
“I expected my website traffic to split evenly across three landing pages — 100 visitors each — but actually got [112, 88, 100]. Can you do a chi-square goodness-of-fit test to check if that deviation is statistically significant?”
Technical details
Endpoint
POSThttps://kihustle.tech/bot-bazaar/api/v1/chi-square-goodness?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cheap ($0.002 USDC) stateless chi-square goodness-of-fit computation via a pay-per-call API without setting up your own statistical library or environment. Best for agents that need on-demand hypothesis testing in automated workflows where spinning up a Python/R environment is impractical.
Side effects
- Mismatched array lengths between expected and observed inputs may cause computation errors
- Empty or null arrays will likely result in a failed status
- Non-numeric values in arrays may be rejected
- Arrays with a single element cannot produce meaningful chi-square results
- Network or payment processing failures returning non-200 responses
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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