Statistical Inference API

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.