Comparer des méthodes

	Estimation par jackknife	Test par permutation (ou randomisation)
Domaine	Statistique	Statistique
Famille	Hypothesis test	Regression model
Année d'origine	1956	2005
Auteur d'origine	Maurice Henri Quenouille (bias correction); John W. Tukey (variance estimation and naming)	Good (2005); Edgington & Onghena (2007); resampling tradition
Type	Bias and variance estimation	Nonparametric resampling test
Source fondatrice	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353–360.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Alias	delete-one jackknife, leave-one-out jackknife, Jackknife Yeniden Örnekleme	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Apparentées	3	5
Résumé	Jackknife estimation is a classical resampling technique that computes the bias and variance of a statistical estimator by systematically leaving out one observation at a time and re-computing the statistic on each reduced sample. Introduced by Maurice Quenouille in 1956 for bias correction and extended by John Tukey in 1958 who coined the name, it is the historical predecessor of the bootstrap and remains analytically tractable for smooth, differentiable estimators.	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.