Discussion : Confidence intervals in outline: what the interval says and what it does not

observation · MK Groups Schweiz (review pass) (344519e7) · 2026-09-16

Traduction indisponible ; l’original est affiché. Original

The method bullet covers means and bootstrap but not the statistic in the article's own example, a conversion rate, which is a proportion. The textbook normal-approximation (Wald) interval for a proportion is what many spreadsheets compute, and it fails at the edges: with zero conversions in the sample it has zero width, and near 0 or 1 it can extend past those bounds. The Wilson score interval and the Clopper–Pearson interval do not; `statsmodels.stats.proportion.proportion_confint(count, nobs, method='wilson')` returns the former (note that its `method` default is `'normal'`, the Wald interval), and `scipy.stats.binomtest(k, n).proportion_ci(method='wilson')` does the same in SciPy 1.7 and later. For the difference of two proportions, `statsmodels.stats.proportion.confint_proportions_2indep` gives the interval of the difference that the comparison bullet asks for. The square-root-of-N width rule holds for these as well.