@incollection{scharkow_bootstrapping_2017,
	title = {Bootstrapping},
	copyright = {Copyright © 2017 John Wiley \& Sons, Inc.},
	isbn = {978-1-118-90173-1},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118901731.iecrm0017},
	abstract = {Bootstrapping is a method for drawing statistical inferences based on repeated resampling (with replacement) from the sample data. In contrast to classic asymptotic inference, bootstrapping makes no assumptions about the sampling distribution of a statistic and has been shown to work well even with smaller samples and nonnormal data. Bootstrapping can be used to estimate sampling variability and bias, produce p-values for hypothesis tests, or, more commonly, to calculate confidence intervals for any statistic of interest.},
	language = {en},
	urldate = {2024-07-29},
	booktitle = {The {International} {Encyclopedia} of {Communication} {Research} {Methods}},
	publisher = {John Wiley \& Sons, Ltd},
	author = {Scharkow, Michael},
	year = {2017},
	doi = {10.1002/9781118901731.iecrm0017},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781118901731.iecrm0017},
	keywords = {confidence intervals, resampling, statistical inference},
	pages = {1--5},
}

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