Linked from

The 54 pages that link to Confidence interval, each with the reason it gives.

• Randomized controlled trialRelated: It conveys the precision and uncertainty of an estimated trial effect.

• Monte Carlo methodRelated: It expresses sampling uncertainty around a Monte Carlo estimate.

• Measurement uncertaintyCompared with: Its frequentist coverage meaning differs from a measurement coverage interval.

• Sampling distributionRelated: Confidence intervals use sampling distributions to quantify the uncertainty of an estimate.

• Normal distributionRelated: Normal quantiles set interval widths when estimates have approximately normal sampling distributions.

• Central limit theoremRelated: Normal approximations from the theorem underpin many large-sample confidence intervals.

• Statistical inferenceBroader topic: It expresses estimation uncertainty through a range with a long-run coverage guarantee.

• Likelihood functionRelated: Likelihood-based procedures can produce confidence intervals by identifying parameter values compatible with the data.

• P-valueCompared with: Intervals show a range of parameter values, while a p-value summarizes a tail probability.

• Statistical powerRelated: Intervals show effect-size uncertainty and can be more informative than a power label alone.

• Statistical significanceRelated: Intervals show estimate precision that a significant-or-not label conceals.

• Intelligence quotientRelated: A test score is an estimate, so its precision is better represented by a range than a point alone.

• Regression analysisRelated: Regression intervals quantify uncertainty in estimated coefficients.

• UncertaintyBroader topic: It expresses sampling uncertainty around an estimated quantity.

• Significant figuresCompared with: Intervals communicate inferential uncertainty more fully than a digit count.

• Uncertainty quantificationCompared with: It is a frequentist interval summary, distinct from a Bayesian probability statement.

• A/B testingRelated: Intervals show the range of effect sizes compatible with the experiment's data and method.

• Null hypothesisRelated: Intervals show effect sizes compatible with data, adding information a reject-or-not decision can conceal.

• Effect sizeRelated: Intervals show the uncertainty around an estimated effect size.

• Bayesian statisticsCompared with: Unlike a credible interval, it does not assign a probability to the fixed parameter being inside this interval.

• Sampling errorRelated: Its width reflects sampling uncertainty under the assumed model or design.

• Standard errorRelated: Many confidence-interval formulas use a standard error to set the interval’s width.

• StatisticsRelated: It expresses estimation uncertainty through a range with a precise long-run interpretation.

• PrevalenceRelated: It conveys sampling uncertainty around an estimated prevalence.

• Statistical hypothesis testingRelated: Intervals convey plausible parameter values and precision beyond a binary test decision.

• Excess mortalityRelated: Baseline models and incomplete death counts make excess-mortality estimates uncertain.

• Frequentist statisticsRelated: Its confidence level describes long-run coverage, not a probability distribution for the fixed parameter.

• Posterior probabilityCompared with: It is often mistaken for a posterior probability interval, but its interpretation differs.

• Type I and type II errorsRelated: Confidence levels and two-sided tests are linked through their shared error rates.

• Margin of errorRelated: The margin of error is the distance from the estimate to either endpoint of a symmetric confidence interval.

• Vaccine efficacyRelated: It shows the precision of an efficacy estimate, which a single percentage cannot convey.

• BiostatisticsRelated: Intervals communicate estimate precision beyond a single biomedical point estimate.

• Frequentist probabilityBroader topic: Its stated coverage is a long-run frequency, not a probability assigned to the fixed parameter.

• Jerzy NeymanBroader topic: Neyman developed the modern repeated-sampling interpretation of confidence intervals.

• Prediction intervalCompared with: It targets the mean effect, whereas a prediction interval targets a future study’s effect.

• Student's t-testRelated: A t-based interval expresses plausible values for a mean or mean difference alongside a test.

• Null hypothesis significance testingRelated: Intervals add estimates of plausible effect sizes that a binary test omits.

• Overconfidence effectRelated: People's subjective ranges often fail to achieve their claimed coverage.

• Parameter (statistics)Related: It expresses parameter uncertainty with a range rather than one estimate.

• BiometricsRelated: Intervals express the uncertainty attached to biometric estimates.

• Chi-squared distributionRelated: For normal data, chi-squared quantiles produce confidence intervals for a population variance.

• Frequentist inferenceBroader topic: Its confidence level describes long-run coverage, not a probability assigned to this realized interval.

• Mathematical statisticsBroader topic: Coverage properties let mathematical statistics assess interval procedures across repeated samples.

• Population parameterRelated: It reports uncertainty about a parameter estimate rather than only a single value.

• Credible intervalCompared with: Unlike a credible interval, its coverage statement concerns repeated samples, not posterior probability for fixed bounds.

• Sample size determinationRelated: A desired interval width supplies a direct precision target for sample planning.

• Statistical hypothesis testCompared with: Intervals express compatible parameter values, complementing a test's thresholded decision.

• Correlation coefficientRelated: Intervals express uncertainty around estimated population correlations.

• Parametric statisticsRelated: Parametric uncertainty estimates use the fitted model's sampling behavior.

• Statistical analysisBroader topic: It expresses estimation uncertainty as a range rather than a single value.