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Chi-Square Test (Goodness of Fit & Independence)
x402
Chi-Square Test (Goodness of Fit & Independence) 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).
Runs a chi-square goodness-of-fit test on observed vs expected counts, or a chi-square independence test on a contingency table, returning the test statistic, p-value, degrees of freedom, and effect size.
Price / call$0.02
Success—
StatusUnknown
How this service works
Chi-square goodness of fit (observed vs expected counts) or independence test on a table, with p-value and effect size.
Output
Returns the chi-square statistic, degrees of freedom, p-value, expected counts array, per-cell contributions to chi-square, a binary flag for significance at 5%, number of cells with expected counts below 5 (a validity warning), a plain-language summary, and a verdict string.
Example use cases
Fairness test for a six-sided die
“I rolled a die 60 times and got these counts per face: 5,8,9,8,10,20. Can you run a chi-square goodness-of-fit test against equal expected frequencies and tell me if the die is fair?”
Survey response distribution check
“My survey has 4 options and I expected 25% each, but got 120,80,95,105 responses. Run a chi-square goodness-of-fit test against equal expected counts and give me the p-value and chi-square statistic.”
Independence between gender and product preference
“I have a contingency table of gender vs product choice: 30,10 for men and 15,25 for women. Run a chi-square independence test on this table — rows separated by semicolons: 30,10;15,25 — and tell me if the association is statistically significant.”
Technical details
Endpoint
GEThttps://stats-tests.x402tookit.com/chi-square-test?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to test categorical data: either whether observed counts match a hypothesized distribution (goodness of fit) or whether two categorical variables are independent (contingency table). Prefer this over Fisher's exact test for larger samples; use Fisher's exact endpoint on this same host for small 2x2 tables where exact p-values are needed.
Side effects
- Missing both 'observed' and 'table' parameters returns an error
- Misformatted table string (wrong delimiter) causes a parse error
- Row lengths inconsistent in the contingency table cause rejection
- All expected counts are zero causes division error
- Very small sample sizes may trigger a warning about cells with expected counts below 5
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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