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	Stima con Jackknife	Cross-Validation	Simulazione Monte Carlo	Test di Permutazione (Randomizzazione)
Campo	Statistica	Processo decisionale	Processo decisionale	Statistica
Famiglia	Hypothesis test	MCDM	MCDM	Regression model
Anno di origine	1956	1974	1949	2005
Ideatore	Maurice Henri Quenouille (bias correction); John W. Tukey (variance estimation and naming)	Stone, M.	Metropolis, N., Ulam, S.	Good (2005); Edgington & Onghena (2007); resampling tradition
Tipo	Bias and variance estimation	Robustness wrapper — k-fold cross-validation for MCDM stability	Robustness wrapper — Monte Carlo uncertainty propagation	Nonparametric resampling test
Fonte seminale	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353–360.	Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society Series B	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association	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
Correlati	3	0	0	5
Sintesi	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.	CROSS-VALIDATION (Cross-Validation — k-fold hold-out validation of MCDM decision consistency) is a ranking multi-criteria decision-making (MCDM) method introduced by Stone, M. in 1974. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	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.