Σύγκριση μεθόδων

	Ακριβής Συμπερασματολογία Τυχαιοποίησης κατά Fisher	Επαναδειγματοληψία Jackknife
Πεδίο	Στατιστική	Στατιστική
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1935	1956
Δημιουργός	Ronald A. Fisher	Quenouille (1956); reviewed by Miller (1974)
Τύπος	Exact permutation-based inference	Resampling / bias and variance estimation
Θεμελιώδης πηγή	Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd.	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.
Εναλλακτικές ονομασίες	fisher randomization test, permutation inference, exact randomization test, randomizasyon çıkarımı (fisher exact randomization)	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme
Συναφείς	5	5
Σύνοψη	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.	The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability.