{"uid":"cap_gbFFIH6fRfLlpjcIDOBv6","slug":"chi-square-goodness-of-fit-test-3344b51d","name":"Chi-Square Goodness-of-Fit Test","description":"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.","url":"https://kihustle.tech/nexus/api/v1/chi-square-goodness","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"expected":{"type":"array","items":{"type":"number"}},"observed":{"type":"array","items":{"type":"number"}}}},"responseSchema":{"type":"json","example":{"result":"processed","status":"success"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","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":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_9jkbPoqw7UYEPI7ksMLFF","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Performs a chi-square goodness-of-fit statistical test comparing observed frequencies to expected frequencies","exampleAgentPrompt":"Run a chi-square goodness-of-fit test for me — my observed counts are [52, 48, 60, 40] and I expected them to be evenly distributed at [50, 50, 50, 50].","exampleUseCases":[{"title":"Testing fairness of a die","prompt":"I rolled a die 120 times and got these counts per face: [18, 22, 19, 21, 17, 23]. Expected is 20 for each face. Can you run a chi-square goodness-of-fit test to tell me if the die is fair?"},{"title":"Validating survey response distribution","prompt":"My survey has 4 response options and I expected responses to be split 25% each, but I got [45, 30, 15, 10] out of 100 responses. Run a chi-square test to see if the distribution is significantly different from expected."},{"title":"Checking if sales match forecast","prompt":"I forecasted sales per category as [200, 150, 100, 50] units but actually sold [210, 140, 95, 55]. Can you do a chi-square goodness-of-fit test to see if the actual sales match my forecast?"}],"resultDescription":"Returns a JSON object with a result field indicating whether the chi-square test was processed and a status field confirming success. Note: the response schema is minimal and may not include the full chi-square statistic, p-value, or degrees of freedom in the current implementation.","failureModes":["Mismatched array lengths between observed and expected arrays may cause errors","Non-numeric values in arrays will likely cause computation failure","Empty arrays may return an error or unexpected result","Expected frequencies of zero could cause division-by-zero issues in chi-square calculation","Very large arrays may time out"],"whenToPreferThis":"Use this endpoint when you need a quick, cheap statistical computation to test whether observed categorical frequencies match a theoretically expected distribution. Suitable for automated pipelines needing hypothesis testing at scale without spinning up a local statistics library.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T00:57:50.903Z","isFirstParty":false}