{"uid":"cap_9xXDfqC-udWtbFjxMhtfl","slug":"kihustle-median-absolute-deviation-calculator-6754369e","name":"KiHustle Median Absolute Deviation Calculator","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/json-median-abs-deviation?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"values":{"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_Ih9hLUH_g-tZaNGcMp0sw","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":"Computes the median absolute deviation (MAD) of a given array of numeric values","exampleAgentPrompt":"Can you calculate the median absolute deviation for this set of numbers: [12.5, 14.1, 13.8, 99.2, 13.0, 14.5, 12.9]? I want to detect if that 99.2 is an outlier.","exampleUseCases":[{"title":"Outlier detection in sensor readings","prompt":"I have a list of temperature sensor readings from today: [22.1, 22.4, 21.9, 22.3, 87.5, 22.0, 22.2]. Can you compute the median absolute deviation to help me figure out if 87.5 is a real outlier?"},{"title":"Financial data dispersion analysis","prompt":"Here are daily revenue figures for the past two weeks: [1200, 1350, 1280, 1410, 1190, 1320, 4800, 1300, 1250, 1380, 1290, 1340, 1210, 1360]. Calculate the median absolute deviation so I can understand the spread and flag any anomalies."},{"title":"Data quality check before ML training","prompt":"Before I train my model, I need to check the variability in this feature column: [0.82, 0.79, 0.84, 0.81, 0.83, 0.02, 0.80, 0.78]. Can you compute the MAD so I can decide whether to drop that suspicious 0.02 value?"}],"resultDescription":"Returns a JSON object with the computed median absolute deviation result and a success status, e.g. {result: <MAD value>, status: 'success'}","failureModes":["Empty array input returns an error or undefined result","Non-numeric values in array cause processing failure","Malformed request body (missing 'values' key) results in error","Single-element array returns MAD of 0 (trivial but valid)","Network or payment authorization failure returns 402 or 5xx"],"whenToPreferThis":"Choose this endpoint when you need a robust, outlier-resistant measure of statistical dispersion for a numeric dataset. MAD is preferred over standard deviation when your data may contain outliers, as it is less sensitive to extreme values. Ideal for anomaly detection, data quality validation, and preprocessing steps in ML pipelines.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-13T12:47:56.687Z","isFirstParty":false,"canonicalSlug":"kihustle-median-absolute-deviation-calculator-019d9a9f"}