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Numora Confidence Interval Computation
x402
Numora Confidence Interval Computation is a paid API for AI agents from numomo.vercel.app, paid per call via x402, $0.1/call, status unknown (last checked 2026-09-15).
Computes the confidence interval for a dataset at a specified confidence level (e.g., 95%)
Price / call$0.1
Success—
StatusUnknown
How this service works
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.
Output
Returns a JSON object with success flag, a result object containing confidence interval bounds (lower and upper limits), margin of error, sample statistics (mean, standard deviation, sample size), and a human-readable computation string describing what was calculated.
Example use cases
Survey margin of error calculation
“I ran a survey and got these satisfaction scores: [7, 8, 6, 9, 7, 8, 5, 9, 8, 7, 6, 8]. Can you compute the 95% confidence interval so I know the margin of error?”
Clinical trial measurement uncertainty
“Here are the blood pressure readings from my study participants: [120, 135, 128, 142, 119, 131, 126, 138, 122, 130]. Calculate the 99% confidence interval for the true mean blood pressure.”
A/B test result reliability check
“My A/B test produced these conversion rates across sessions: [0.042, 0.038, 0.051, 0.044, 0.039, 0.047, 0.041, 0.050]. What's the 90% confidence interval so I can tell if the result is statistically reliable?”
Technical details
Endpoint
POSThttps://numomo.vercel.app/api/statistics/confidenceWhen to prefer
Choose this endpoint when you need a fast, serverless, dependency-free confidence interval calculation on a numeric dataset without integrating a full statistical library. Ideal for AI agents that need on-the-fly statistical validation of sample data, A/B test results, or measurement sets. Prefer this over rolling your own math or calling a general-purpose compute engine when you want a clean, structured JSON result with human-readable explanation. Not suitable if you need Bayesian credible intervals or require visualization outputs.
Side effects
- Empty or missing data array returns validation error
- Confidence level outside valid range (0-1) returns error
- Non-numeric values in data array cause computation failure
- Single-element array may return undefined interval or error
- Malformed request body returns 400 error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| datarequired | array | |
| level | number | Confidence level (e.g., 0.95) |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| resultrequired | object | Computation result varies by endpoint |
| successrequired | boolean | Always true on success |
| computationrequired | string | Human-readable description of what was computed |
Tags
x402
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