Data Science Statistics
Bootstrap sampling is a statistical technique that involves repeatedly resampling a dataset with replacement to estimate the distribution of a statistic. This method is particularly useful for assessing the accuracy and stability of estimates when the underlying distribution is unknown or when the sample size is small. By creating multiple bootstrap samples, analysts can derive confidence intervals and conduct hypothesis testing, which helps in variable selection and model building.
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