The central limit theorem is one of the most useful concepts in statistics. The theorem is all about drawing samples of a finite size $n$ from a population. The theorem states that if one collects samples of a large enough sample size $n$, and calculates each sample's mean (or sum), the shape of the histogram of those means (or sums) approximates a normal bell shape. The usefulness of the central limit theorem is due to the fact, that it does not matter what the distribution of the original population is, the distribution of sample means and the sums tend to follow the normal distribution.

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The E-Learning project SOGA-Py was developed at the Department of Earth Sciences by Annette Rudolph, Joachim Krois and Kai Hartmann. You can reach us via mail by soga[at]zedat.fu-berlin.de.

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You may use this project freely under the Creative Commons Attribution-ShareAlike 4.0 International License.

Please cite as follow: Rudolph, A., Krois, J., Hartmann, K. (2023): Statistics and Geodata Analysis using Python (SOGA-Py). Department of Earth Sciences, Freie Universitaet Berlin.