**Bootstrapping** is a technique used to make estimations from data by taking an average of the estimates from smaller data samples.

# Method
The bootstrap method involves iteratively resampling a dataset with replacement. Instead of only estimating our statistic once on the complete data, we can do it many times on a re-sampling (with replacement) of the original sample. Repeating this re-sampling multiple times allows us to obtain a vector of estimates. We can then compute variance, expected value, empirical distribution, and other relevant statistics of these estimates.



# Uses
Bootstrapping allows statistical inferences to be made about the population distribution of a small sample. It is used to account for distortions caused by certain sample data that could make for a bad representation of the overall data.

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What is the Bootstrap method in data science?

Bootstrapping is a technique used to make estimations from data by taking an average of the estimates from smaller data samples. Method The bootstrap method involves iteratively resampling a dataset with replacement. Instead of only estimating our statistic once on the complete data, we can do it many times on a re-sampling (with replacement) of the original sample. Repeating this re-sampling multiple times allows us to obtain a vector of estimates. We can then compute variance, expected value, empirical distribution, and other relevant statistics of these estimates. 