방법 비교

	피셔의 정확 무작위화 추론	부트스트랩 추론	잭나이프 재표본 추출	최소제곱법(OLS) 회귀
분야	통계학	통계학	통계학	계량경제학
계열	Regression model	Regression model	Regression model	Regression model
기원 연도	1935	1979	1956	2019
창시자	Ronald A. Fisher	Bradley Efron	Quenouille (1956); reviewed by Miller (1974)	Wooldridge (textbook treatment); classical least squares
유형	Exact permutation-based inference	Resampling-based inference	Resampling / bias and variance estimation	Linear regression
원전	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
별칭	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	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
관련	5	5	5	5
요약	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.	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).