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	Nadzorowana inferencja permutyacyjna Fishera	Estymacja bootstrapowa	Resampling Jackknife
Dziedzina	Statystyka	Statystyka	Statystyka
Rodzina	Regression model	Regression model	Regression model
Rok powstania	1935	1979	1956
Twórca	Ronald A. Fisher	Bradley Efron	Quenouille (1956); reviewed by Miller (1974)
Typ	Exact permutation-based inference	Resampling-based inference	Resampling / bias and variance estimation
Źródło pierwotne	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.	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.
Inne nazwy	fisher randomization test, permutation inference, exact randomization test, randomizasyon çıkarımı (fisher exact randomization)	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme
Pokrewne	5	5	5
Podsumowanie	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.	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.