{"uid":"cap_flYYoVD4Olf_UTHajTQ6b","slug":"numora-chi-square-statistics-endpoint-1f4efe8f","name":"Numora Chi-Square Statistics Endpoint","description":"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.","url":"https://numomo.vercel.app/api/statistics/chisquare","method":"POST","headers":{},"bodySchema":{"type":"object","required":["observed"],"properties":{"expected":{"type":"array"},"observed":{"type":"array"}}},"responseSchema":{"type":"object","required":["success","result","computation"],"properties":{"result":{"type":"object","description":"Computation result varies by endpoint"},"success":{"type":"boolean","description":"Always true on success"},"computation":{"type":"string","description":"Human-readable description of what was computed"}}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.1","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"registry","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.1/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_VI01qUZY0hKRMZ2fsepYh","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.1","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"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.","exampleAgentPrompt":"Run a chi-square goodness-of-fit test on these observed counts [120, 85, 95] against the expected counts [100, 100, 100] and tell me the chi-square statistic, p-value, and degrees of freedom.","exampleUseCases":[{"title":"Survey response distribution test","prompt":"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?"},{"title":"A/B test categorical outcome analysis","prompt":"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."},{"title":"Genetics ratio hypothesis test","prompt":"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?"}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T00:38:23.688Z","isFirstParty":false}