Then to find the mean, instead of considering all the 1000 entries you can take 50 samples of size 4 each and calculate the mean for each sample. Instead of looking at the entire population, we look at multiple subsets all of the same size taken from the population.įor example, if your population size is 1000. This basically means that bootstrap sampling is a technique using which you can estimate parameters like mean for an entire population without explicitly considering each and every data point in the population. In statistics, Bootstrap Sampling is a method that involves drawing of sample data repeatedly with replacement from a data source to estimate a population parameter. The definition for bootstrap sampling is as follows : In this tutorial, we will learn what is bootstrapping and then see how to implement it. This is a tutorial on Bootstrap Sampling in Python.
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