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	Bootstrapinferens	Medianabsolutavvikelse (MAD) – estimering	Permutationstest (Randomiseringstest)
Ämnesområde	Statistik	Statistik	Statistik
Familj	Regression model	Regression model	Regression model
Ursprungsår	1979	1974	2005
Upphovsperson	Bradley Efron	Hampel (influence-curve treatment); classical robust statistics	Good (2005); Edgington & Onghena (2007); resampling tradition
Typ	Resampling-based inference	Robust scale estimator	Nonparametric resampling test
Ursprungskälla	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Alias	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Närliggande	5	5	5
Sammanfattning	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.	Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the 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.