Bootstrap: A Beginner-Friendly Introduction With a Python example

Author(s): Janik and Patrick Tinz

Originally published on Towards AI.

Bootstrapping and the central limit theorem

Photo by Glenn Carstens-Peters on Unsplash

The bootstrap method is a resampling technique in which one draws many samples again from one sample. It is used to estimate summary statistics like the mean or standard deviation. The bootstrap method is a powerful statistical tool, and it is useful for small samples. The advantage of the bootstrap method is that this method does not make any distribution assumptions. It is a non-parametric method and can also use when normal distribution assumptions of the model are not applicable.

The bootstrap method works as follows:

Take k samples with replacement from a given dataset.For each sample,… Read the full blog for free on Medium.

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Published via Towards AI