**population distribution** is the probability distribution derived from the knowledge of all elements of a population (Mann 2012). We know that depending on the population of interest the random variable of interest may be a discrete variable. A discrete variable is at least in principle countable. As another case, the random variable of interest may be a continuous variable, and thus it is a variable that can take any value within a given interval. Both, the discrete and the continuous probability distribution may be described by statistical parameters, such as the mean, the standard deviation the median, the mode, among others. However, these parameters describing the population are **always constant**, because the population is the set of all elements and thus population statistics do not change. For example, for any population data set, there is **only one value** of the population mean, **one value** for the standard deviation and so on.

**Citation**

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.

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.*