Comparer des méthodes

	Simulation Bootstrap	Test par permutation (ou randomisation)
Domaine	Simulation	Statistique
Famille	Process / pipeline	Regression model
Année d'origine	1979	2005
Auteur d'origine	Bradley Efron	Good (2005); Edgington & Onghena (2007); resampling tradition
Type	Simulation-based nonparametric inference	Nonparametric resampling test
Source fondatrice	Efron, B. & Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Alias	bootstrap resampling, empirical resampling, nonparametric bootstrap, Önyükleme Simülasyonu (Bootstrap Resampling)	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Apparentées	5	5
Résumé	Bootstrap simulation, introduced by Bradley Efron in 1979, is a simulation-based inference method that derives the sampling distribution of virtually any statistic by repeatedly resampling with replacement from the observed data. Because it requires no parametric distributional assumptions, it provides a robust, general-purpose alternative to analytical confidence intervals and parametric hypothesis tests across continuous, ordinal, binary, and count data.	The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value.