Bootstrapping involves using limited sample data to represent the entire population through repeated sampling.

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Bootstrapping is a resampling technique in which multiple small samples are taken from a larger data set to infer parameters that describe the population from which the sample was drawn. It is used to estimate population parameters such as mean, standard deviation, and other statistical measures. It is based on the assumption that the small sample is representative of the larger population, and the validity of the results depends on the quality of the original sample. Therefore, bootstrapping involves using limited sample data to represent the entire population through repeated sampling.

Bootstrapping Involves Using Limited Sample Data to Represent the Entire