Comparer des méthodes

	Recherche par test d'hypothèse assisté par simulation	Test par permutation (ou randomisation)
Domaine	Conception de la recherche	Statistique
Famille	Process / pipeline	Regression model
Année d'origine	1980s–1990s (bootstrap: 1979; permutation inference: mid-20th century; unified simulation-assisted framing: 1990s–2000s)	2005
Auteur d'origine	Bradley Efron (bootstrap framework); Phillip Good (permutation tests); Monte Carlo tradition traced to Stanislaw Ulam and John von Neumann	Good (2005); Edgington & Onghena (2007); resampling tradition
Type	Quantitative research design integrating computational simulation with classical hypothesis testing	Nonparametric resampling test
Source fondatrice	Efron, B., & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC. ISBN: 978-0412042317	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Alias	simulation-based hypothesis testing, Monte Carlo hypothesis testing, computational hypothesis testing, simulation-assisted inference	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Apparentées	3	5
Résumé	Simulation-assisted hypothesis testing research replaces or supplements analytical probability theory with computational simulation — resampling, permutation, or Monte Carlo methods — to construct null distributions and evaluate hypotheses. Rather than assuming a parametric distribution and consulting a table, the researcher generates thousands of simulated datasets from the observed data or a specified model, building an empirical null distribution against which the observed test statistic is compared. The approach is especially valuable when analytic assumptions (normality, large samples) cannot be met.	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.