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The 45 pages that link to Sampling distribution, each with the reason it gives.

• Monte Carlo methodRelated: It describes the uncertainty in an estimate produced by repeated Monte Carlo runs.

• Maximum likelihood estimationRelated: It describes how an estimator varies across repeated data sets, distinct from the likelihood of one observed set.

• Central limit theoremRelated: The theorem explains why many sample means have approximately normal sampling distributions.

• Statistical inferenceRelated: It describes how much an estimate would vary across repeated samples.

• Likelihood functionRelated: The sampling design determines which probability model supplies the likelihood for the observed sample.

• Linear regressionRelated: It underlies confidence intervals and tests for regression coefficients.

• Statistical powerRelated: Power is the alternative distribution's probability mass inside the test's rejection region.

• Survey samplingRelated: It describes the variability that sampling introduces into survey estimates.

• EconometricsRelated: It underlies confidence intervals and tests for estimated economic relationships.

• RandomizationRelated: Randomization creates a distribution of possible assignment-based estimates.

• Parameter estimationRelated: It describes how an estimator varies from sample to sample.

• Statistical hypothesis testingRelated: It supplies the reference distribution used to judge the observed statistic.

• Statistical process controlRelated: It underlies the calculation of control limits for plotted sample statistics.

• Frequentist statisticsRelated: Frequentist guarantees describe how statistics vary across hypothetical repetitions of the sampling process.

• Pearson correlation coefficientRelated: It describes how an estimated correlation varies from sample to sample.

• Sufficient statisticRelated: Its parameter dependence helps characterize what a statistic reveals about the model.

• Type I and type II errorsRelated: Error probabilities come from how test statistics vary across repeated samples.

• Sample sizeRelated: Larger samples generally make a statistic’s sampling distribution narrower.

• BiostatisticsRelated: It underlies standard errors, confidence intervals, and many tests used in life sciences.

• Jerzy NeymanRelated: Repeated-sampling distributions underpin Neyman’s definitions of confidence and test performance.

• Null hypothesis significance testingRelated: Under the null, it determines how unusual the observed test statistic would be.

• Parameter (statistics)Related: Inference uses this distribution to connect sample statistics with unknown parameters.

• Probability samplingRelated: Design-based uncertainty is described by how estimates vary across possible samples.

• Sample (statistics)Related: It describes how estimates from samples vary across repeated selections.

• Mathematical statisticsRelated: It quantifies how an estimate would vary if the sampling process were repeated.

• Population parameterRelated: It describes how estimates of a fixed parameter vary from sample to sample.

• Trygve HaavelmoRelated: It connects an estimated economic relationship to uncertainty about its value.

• Correlation coefficientRelated: It explains how much a sample correlation varies across repeated studies.

• Lehmann–Scheffé theoremRelated: Completeness and estimator variance are defined through sampling distributions.

• Parametric statisticsRelated: Inference depends on how estimates or test statistics vary across repeated samples.

• Basu's theoremRelated: Completeness and ancillarity are defined through the statistic's sampling distribution.