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Measurement Uncertainty – Binomial Confidence Intervals
x402
Measurement Uncertainty – Binomial Confidence Intervals is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.012002/call, status unknown (last checked 2026-11-02).
Computes probabilities and binomial confidence intervals for shot-count arrays, returning uncertainty metrics with configurable interval method and independence assumptions.
Price / call$0.012002
Success—
StatusUnknown
How this service works
For agents reviewing shot-count results: return probabilities and binomial confidence intervals with the interval method and independence assumptions. New modular adapter: retained result recovery for 24 hours, minimal receipt metadata for 90 days. Existing live route unchanged.
Output
Returns computed probabilities and binomial confidence intervals for each element in the submitted counts array, including interval bounds and uncertainty metrics derived from the specified independence assumptions and interval method. Results are retained for 24 hours; minimal receipt metadata is retained for 90 days.
Example use cases
A/B test result uncertainty analysis
“I ran an A/B test and got these shot counts for each variant: [320, 280, 415, 190] — can you compute the binomial confidence intervals and probability estimates so I understand the measurement uncertainty in the results? The data is public.”
Clinical trial count validation
“We have observed event counts from a synthetic trial dataset: [44, 61, 38, 55, 70] — please calculate the binomial confidence intervals and measurement uncertainty for these counts so we can report the statistical bounds.”
Survey response uncertainty quantification
“Here are response counts from a public survey across 5 questions: [512, 489, 601, 445, 533] — I need the probability estimates and confidence intervals to understand how uncertain these shot-count results are.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/compute/quantum/measurement-uncertainty?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, per-call statistical computation of binomial confidence intervals and probabilities for shot-count arrays (up to 32 elements) with explicit uncertainty quantification. It is appropriate when your data is classified as either public or synthetic and you need bounded uncertainty metrics rather than raw summary statistics. Prefer this over generic statistics APIs when you specifically need binomial interval methods with independence assumptions applied to count data.
Side effects
- Array exceeds 32 items (maxItems constraint violated)
- Empty array submitted (minItems of 1 not met)
- Invalid dataClass value not in enum ['public','synthetic']
- Non-integer or negative values in counts array
- Payment not included or insufficient (x402 payment required)
- Network or provider timeout returning 5xx error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| counts | array | |
| dataClass | string |
Tags
x402
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