# Confidence Interval Estimation > Use when you must estimate a confidence interval for a sample statistic: the Student t interval for a sample mean, the pooled or Welch-Satterthwaite interval for a difference of means, and the chi-square interval for the variance and standard deviation of a measured or production batch, each at a stated confidence level. Quantiles come from in-leaf inversion: the two-sided t quantile by bisection on the regularized incomplete beta relation and the chi-square quantile by lower incomplete gamma inversion, pure Python stdlib. Produces interval bounds, standard error, degrees of freedom, and quantiles as keyed dicts for measurement, drag-count, and production statistics. Trigger: t confidence interval, chi square variance interval, mean difference interval, quantile inversion, small sample statistics, interval estimation. - Skill: `ashfordeou/confidence-interval-estimation` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add ashfordeou/confidence-interval-estimation` - Raw SKILL.md: https://api.skillmd.com/api/skills/ashfordeou/confidence-interval-estimation/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: Apache-2.0 - Author: ashfordeOU (https://skillmd.com/u/ashfordeou) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ashfordeou/confidence-interval-estimation --- # Confidence Interval Estimation (cross-cutting/numerics/confidence-interval-estimation) Use when you must estimate a parametric confidence interval for a sample statistic: the exact small-sample t interval around a measured mean, the pooled or Welch interval around a difference of means (configuration A versus B, batch versus batch), and the chi-square interval around a variance or standard deviation of a production batch, all at a stated confidence level. This leaf implements the interval counterpart to the hypothesis-testing verdict layer: every quantile comes from the module's own inversion machinery (two-sided t quantile by bisection on the regularized incomplete beta identity, chi-square quantile by bisection on the lower incomplete gamma function), pure Python stdlib with no external statistics packages. It pairs with cross-cutting/numerics/hypothesis-testing for the verdict on the same data: the interval that excludes zero is the verdict that rejects. ## Domain quick reference - Conventions: level is the two-sided confidence level (0.95 for 95%), alpha = 1 - level. Sample x of size n, mean xbar, sample standard deviation s with the n-1 denominator. - Mean interval: xbar +/- t_{1-alpha/2, n-1} * s / sqrt(n). - Difference of means: (m1 - m2) +/- t_{1-alpha/2, df} * se, with the pooled standard error se = sp * sqrt(1/n1 + 1/n2), pooled variance sp^2 = ((n1-1)s1^2 + (n2-1)s2^2) / (n1+n2-2) and df = n1+n2-2 for equal_var True; the Welch standard error se = sqrt(s1^2/n1 + s2^2/n2) with the Welch-Satterthwaite df for equal_var False. - Variance interval: [(n-1)s^2 / chi2_{1-alpha/2, n-1}, (n-1)s^2 / chi2_{alpha/2, n-1}], with chi2_{p, df} the quantile of the chi-square distribution at probability p; the sigma interval is the square root of each variance bound. - Quantile machinery: t_ppf_two_sided(level, df) targets the CDF value p = (1 + level)/2 (the t_{1-alpha/2} tail point) and bisects the identity P(T <= t) = 1 - 0.5 * I_x(df/2, 1/2) with x = df/(df + t^2); chi2_ppf(p, df) bisects P(df/2, x/2), the regularized lower incomplete gamma at x/2. Both bisections run to an absolute tolerance of 1e-9 on the quantile. Regularized incomplete beta and lower incomplete gamma are implemented in-leaf (Lentz continued fraction and series). - NACA-TR-824 frames the statistical-methods context; the relations above are standard engineering methodology, summary-only. ## Workflow 1. State the quantity of interest (mean, difference of means, variance or standard deviation) and fix the two-sided confidence level, default 0.95. 2. For a single measured sample x, call confidence_interval_mean(x, level) and read lower and upper, with the standard error, df and t quantile in the returned dict. 3. For configuration A versus B, call confidence_interval_mean_difference(a, b, level, equal_var) with equal_var True for the pooled interval or False for the Welch interval; the sign of the pair (mean_diff, lower, upper) tells the direction and magnitude of the difference. 4. For a variance or standard deviation of a production batch, call confidence_interval_variance(x, level) and read lower/upper for the variance and sigma_lower/sigma_upper for the standard deviation. 5. When the interval excludes zero, the difference is significant at the same level (interval/verdict duality with the sibling hypothesis-testing leaf). 6. Format any of the builder dicts with interval_summary for the rounded bound pair, width and a text line. 7. Confirm the deterministic checks with the contract test scripts/test_confidence_interval_estimation.py. ## Worked example Drag-count samples a = [267, 261, 263, 258, 262] and b = [273, 271, 268, 275, 270] (n = 5 each; the hypothesis-testing sibling's data). - t quantiles: t_ppf_two_sided(0.95, 4) = 2.776445; t_ppf_two_sided(0.95, 8) = 2.306004. - chi2 quantiles at df = 4: chi2_ppf(0.025, 4) = 0.484419; chi2_ppf(0.975, 4) = 11.143287. - Mean interval of a at 95%: xbar = 262.2, s = 3.2711, se = 1.4629, df = 4, bounds [258.1384, 266.2616]. - Pooled difference interval at 95%: mean_diff = -9.2, se = 1.8974, df = 8, bounds [-13.5753, -4.8247]. The interval excludes 0, consistent with the sibling's reject verdict on the same data (the duality check). - Welch difference interval at 95%: df = 7.7244 (Welch-Satterthwaite), bounds [-13.6027, -4.7973]. - Variance interval of a at 95%: variance = 10.7, df = 4, bounds [3.8409, 88.3533]; sigma bounds [1.9598, 9.3996]. ## Pitfalls - Treating the 95% level as a probability that this one interval holds the parameter: the level is a property of the procedure - the seeded normal sample interval contains the true mean, larger levels give wider intervals, and larger n at equal variance gives narrower intervals. - Quoting the variance interval as the sigma interval (or vice versa): on sample a the variance bounds are [3.8409, 88.3533] while the sigma bounds are [1.9598, 9.3996], the square roots of the variance bounds. - Choosing pooled over Welch (or the reverse) without checking the variances: the pooled difference interval uses df 8 and bounds [-13.5753, -4.8247], while the Welch interval uses the Welch-Satterthwaite df 7.7244 and bounds [-13.6027, -4.7973]. - Reporting the interval and the hypothesis verdict as independent facts: the pooled interval excluding zero and the sibling leaf's reject verdict on the same data are the same statement in two forms (the duality check). - Quoting a precise variance from a small sample: with n = 5 and df = 4 the variance interval spans [3.8409, 88.3533] around 10.7 - the point estimate alone overstates the knowledge. - Passing degenerate inputs: level outside (0, 1), df below 1, chi2 probability outside [0, 1], empty or single-observation samples, and reversed interval bounds all raise ValueError. ## Verification - Confirm the quantile anchors to 1e-4: t(0.975, 4) = 2.776445, t(0.975, 8) = 2.306004, chi2(0.025, 4) = 0.484419, chi2(0.975, 4) = 11.143287, and the two-sided level-0.95 t quantile equals the one-sided p = 0.975 t quantile. - Confirm the worked bounds: mean CI of a within [258.1384, 266.2616], pooled difference CI within [-13.5753, -4.8247] (excludes 0), variance CI within [3.8409, 88.3533], sigma CI within [1.9598, 9.3996]. - Confirm a seeded normal sample gives a mean interval that contains the true mean, larger levels give wider intervals, and larger n at equal variance gives narrower intervals. - Confirm every call is deterministic run to run and each builder dict carries exactly the documented keys. - Confirm ValueError rejection of level outside (0, 1), df below 1, chi2 probability outside [0, 1], empty samples and single-observation samples, and reversed interval bounds. - Run the contract test offline: python3 scripts/test_confidence_interval_estimation.py (31 tests, deterministic, no network). ## Related leaves - cross-cutting/numerics/hypothesis-testing: the verdict sibling on the same data; the interval excluding zero and the reject verdict are the same statement in two forms (duality). - cross-cutting/numerics/descriptive-statistics: the mean, standard deviation and variance inputs this leaf consumes. - cross-cutting/numerics/uncertainty-propagation: the expanded-uncertainty propagation route built from the partial derivatives of the measurement model, the standard choice for indirect measurements rather than direct sample statistics. - cross-cutting/numerics/monte-carlo-sampling: intervals read directly from the spread of many generated draws of a stochastic model, the sampling-based alternative for non-parametric cases. - cross-cutting/numerics/probability-distributions: distribution characterization and fitting context for the underlying model. ## Behavior contract (gate 3) Run the deterministic contract test (stdlib unittest, offline): python3 scripts/test_confidence_interval_estimation.py The test covers the worked quantile anchors and interval bounds above (t and chi2 to 1e-4, intervals to 1e-4), the mean/pooled/Welch/variance interval builders on the drag-count data, the pooled interval excluding zero (duality), the sigma square-root relation, the symmetric-about-the- statistic identity, level and sample-size monotonicity, seeded normal-sample coverage of the true mean, variance and difference, determinism, the exact key sets of every builder dict, the interval_summary rounding contract, and ValueError rejection of non-physical levels, df, probabilities, empty and single-observation samples and reversed bounds. Runs in well under a second. ## Compliance - Standards referenced, not reproduced: NACA-TR-824 frames the statistical-methods convention; the t, Welch and chi-square interval relations above are standard engineering methodology, summary-only per standards-map.yaml. - compliance: STANDARDS-REF, gated: false.