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API · /inference-api

Statistical Inference API

healthy 4,030 Subscribers

Inferential-statistics maths as an API, computed locally and deterministically. The samplesize endpoint computes how many respondents a survey or experiment needs for a proportion, n = Z²·p(1−p)/E², from a confidence level and a margin of error (using p = 0.5 for the most conservative size), with a finite-population correction when the population is known. The confidence endpoint builds a confidence interval for a mean (estimate ± Z·σ/√n) or a proportion (p ± Z·√(p(1−p)/n)), returning the standard error, margin of error and the lower and upper bounds. The ztest endpoint runs a one-sample z-test, z = (x̄ − μ₀)/(σ/√n), and returns the z-score, the one- or two-tailed p-value and whether the result is significant at the chosen alpha. The z-scores come from an exact inverse-normal and the p-values from the normal CDF. Everything is computed locally and deterministically, so it is instant and private. Ideal for A/B-testing, survey, research and analytics app developers, experiment dashboards and data-science tools, and education. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 3 endpoints. This is inferential statistics; for descriptive statistics use a statistics API and for probability distributions use a probability API.

#inference #sample-size #confidence-interval #z-test #statistics #developer-tools
api.oanor.com/inference-api
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Machine-readable spec so AI agents can integrate this API.

/api/inference-api/openapi.json
/api/inference-api/llms.txt

Discovery: GET /api/index.json lists every API.

API health

healthy
Uptime
100.00%
Server probes · 24h
Avg latency
93 ms
Server probes · 24h
Subscribers
4,030
active
Total calls
32
last 7 days
status Full status page → · 24 probes/24h

Pricing

Pick a tier — billed monthly, cancel anytime.

Free

Free

  • 3,000 calls / month
  • 2 requests / second
  • Hard cap (429 above quota, no overage)
  • Sample-size & confidence-interval endpoints
  • Single z-test per call
  • Deterministic local compute, no data cost
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Starter

€5.00 /month

  • 40,000 calls / month
  • 5 requests / second
  • Hard cap (429 above quota, no overage)
  • All inference endpoints
  • One- & two-proportion z-tests
  • Margin-of-error & power inputs
  • Email support
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Pro

€15.00 /month

  • 250,000 calls / month
  • 15 requests / second
  • Hard cap (429 above quota, no overage)
  • High-throughput survey-design batches
  • Finite-population correction
  • Confidence levels 80-99.9%
  • Priority support
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Mega

€49.00 /month

  • 1,571,000 calls / month
  • 40 requests / second
  • Hard cap (429 above quota, no overage)
  • Bulk experiment power analysis
  • Unlimited z-test variants
  • Lowest per-call cost at scale
  • SLA-backed availability
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Related APIs

Other APIs with overlapping tags.

Sample Size API

Survey and poll sample-size planning as an API, computed locally and deterministically. The proportion endpoint computes the number of respondents needed to estimate a proportion within a target margin of error at a chosen confidence level, n = z²·p(1−p)/E², defaulting to the worst-case p = 0.5 that maximises the required size, with an optional finite-population correction n/(1 + (n−1)/N) for a known population — the classic ±5 % margin at 95 % confidence needs 385 responses, ±3 % needs 1 068, and capping the population at 1 000 cuts the ±5 % requirement to 278. The mean endpoint sizes a sample for estimating a mean to within a margin of error from the standard deviation, n = (z·σ/E)². The margin endpoint inverts the relationship, returning the margin of error a given sample size actually achieves. The critical z-value is computed from the confidence level with a high-accuracy inverse-normal so any confidence works, not just the textbook 90/95/99 %. Margins, proportions and confidence are decimals (0.05, 0.5, 0.95). Everything is computed locally and deterministically, so it is instant and private. Ideal for market-research, polling, UX-research, survey-platform, product-analytics and statistics-education app developers, study-planning and sample-size tools, and research software. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 3 endpoints. This is sample-size planning with the normal approximation; for A/B-test significance use an A/B-test API and for descriptive statistics a statistics API.

api.oanor.com/samplesize-api

Statistics Calculator API

Descriptive-statistics maths as an API, computed locally and deterministically. The descriptive endpoint summarises a list of numbers — the count, sum, mean, median, mode, minimum, maximum and range, the population and sample variance and standard deviation, and the quartiles Q1/Q2/Q3 with the interquartile range by Tukey's method. The correlation endpoint computes the Pearson correlation coefficient r between two equal-length series — from −1 (perfect inverse) through 0 (none) to +1 (perfect direct) — along with R² and the covariance. The regression endpoint fits a least-squares line y = a + b·x, returning the slope, intercept and R², the equation, and an optional prediction for a given x. Data is accepted as a JSON array or a comma-separated list. Everything is computed locally and deterministically, so it is instant and private. Ideal for data-analysis, dashboard, research and education app developers, reporting and BI tools, and spreadsheet replacements. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 3 endpoints. This is descriptive statistics; for probability distributions and combinatorics use a probability API.

api.oanor.com/statistics-api

Probability API

Probability distributions and combinatorics as an API. The binomial endpoint gives the probability of exactly k successes in n trials (PMF), the cumulative probability up to k (CDF), and the mean, variance and standard deviation. The poisson endpoint does the same for the Poisson distribution from a rate λ. The normal endpoint computes the z-score, probability density, cumulative probability (CDF) and percentile for a value under a normal distribution with any mean and standard deviation — and runs in reverse, turning a probability into the value (the quantile / inverse CDF) and its z-score. The combinatorics endpoint computes combinations (nCr), permutations (nPr) and factorials with exact big-integer arithmetic. Everything is computed locally and deterministically, so it is instant and private. Ideal for data science and statistics, quality control and A/B-test planning, gaming and gambling odds, risk modelling, and statistics education. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 5 endpoints. This is probability theory; for descriptive statistics on a dataset use a statistics API and for general expression evaluation use a math API.

api.oanor.com/probability-api

Statistics API

Run statistics on a list of numbers without a spreadsheet or a stats package. The describe endpoint returns a full summary of a dataset — count, sum, min, max, range, mean, median, mode, the first and third quartiles and interquartile range, population and sample variance and standard deviation, coefficient of variation, geometric and harmonic means, skewness and kurtosis. Get any percentile of a dataset, the Pearson correlation coefficient (and r²) between two equal-length series, and a simple linear regression (slope, intercept, r² and the line equation). Input is a raw array of numbers (JSON or a comma-separated list) — no CSV, no headers. Perfect for analytics, A/B test summaries, sensor and metrics data, dashboards and quick exploratory analysis. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 5 endpoints. Distinct from the mathjs expression engine and from CSV per-column summaries.

api.oanor.com/stats-api

Frequently asked questions

Quick answers about pricing, quotas, and integration.

How do I get an API key for Statistical Inference API?
Sign up for free at oanor.com, generate an API key from the developer dashboard, and call Statistical Inference API with the x-oanor-key header. No credit card needed for the free tier.
What's the rate limit for Statistical Inference API?
Free tier allows 1 request per second. Paid plans scale up to 50 requests per second on the Mega tier. Hard limits return HTTP 429 above the quota — no surprise overage charges.
How much does Statistical Inference API cost?
Statistical Inference API has a free tier with 100 calls / month. Paid plans start at €5.00 / month with higher quotas and faster rate limits.
Can I cancel my subscription anytime?
Yes. Plans are billed monthly and you can cancel anytime from your billing dashboard. No long-term contracts and no cancellation fee.
Is Statistical Inference API GDPR-compliant?
All requests to Statistical Inference API go through our EU-based gateway. Your upstream API key never leaves our server and no personal data is shared with the upstream provider beyond the request you send.

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Code snippets

Sign up to get an API key, then call any path under your slug.

curl https://api.oanor.com/inference-api/SOME_PATH \
  -H "x-oanor-key: oanor_test_..."
const res = await fetch("https://api.oanor.com/inference-api/SOME_PATH", {
  headers: { "x-oanor-key": "oanor_test_..." }
});
const data = await res.json();
$ch = curl_init("https://api.oanor.com/inference-api/SOME_PATH");
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_HTTPHEADER, ["x-oanor-key: oanor_test_..."]);
$response = curl_exec($ch);
import requests
r = requests.get(
    "https://api.oanor.com/inference-api/SOME_PATH",
    headers={"x-oanor-key": "oanor_test_..."},
)
print(r.json())

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