Comparer des méthodes

	Inférence de randomisation exacte de Fisher	Inférence par bootstrap	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Statistique	Statistique	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	1935	1979	2019
Auteur d'origine	Ronald A. Fisher	Bradley Efron	Wooldridge (textbook treatment); classical least squares
Type	Exact permutation-based inference	Resampling-based inference	Linear regression
Source fondatrice	Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	fisher randomization test, permutation inference, exact randomization test, randomizasyon çıkarımı (fisher exact randomization)	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	5	5	5
Résumé	Randomization inference, introduced by Ronald A. Fisher in The Design of Experiments (1935), computes an exact p-value by evaluating a test statistic across all possible treatment assignments under Fisher's sharp null hypothesis. It is regarded as the gold standard for analysing designed experiments because its validity rests on the known assignment mechanism rather than on distributional assumptions.	Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).