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# Bootstrap Methods

The bootstrap method is a computer-based method for assigning measures of accuracy to sample estimates (Efron and Tibshirani 1994). This technique allows estimation of the sample distribution of almost any statistic using only very simple methods (Varian 2005).

Bootstrap methods are generally superior to ANOVA for small data sets or where sample distributions are non-normal.

ANOVA

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## References

Aksenov, S. "Confidence Intervals by Bootstrap." http://library.wolfram.com/infocenter/MathSource/4272/.Chernick, M. R. Bootstrap Methods: A Practitioner's Guide. New York: Wiley, 1999.Davison, A. C. and Hinkley, D. V. Bootstrap Methods and Their Application. Cambridge, England: Cambridge University Press, 1997.Efron, B. and Tibshirani, R. J. An Introduction to the Bootstrap. Boca Raton, FL: CRC Press, 1994.Mooney, C. Z. and Duval, R. D. Bootstrapping: A Nonparametric Approach to Statistical Inference. Sage, 1993. Siniksaran, R. "BootStrapPackage: A Package of Bootstrap Algorithms for Mean, Simple Linear Regression Models, and Correlation Coefficient." http://library.wolfram.com/infocenter/MathSource/815/.Varian, H. "Bootstrap Tutorial." Mathematica J. 9, 768-775, 2005.

## Referenced on Wolfram|Alpha

Bootstrap Methods

## Cite this as:

Weisstein, Eric W. "Bootstrap Methods." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/BootstrapMethods.html