**Inferential statistics** is all about making decisions or judgments about the value of a particular observation or measurement. One of the most commonly used methods for making such decisions is to perform a hypothesis test. A hypothesis is a proposed explanation for a phenomenon. In the context of statistical hypothesis tests, the term hypothesis is a statement about something that is supposed to be true.

A hypothesis test involves two hypotheses: the ** null hypothesis** and the

The hypothesis test is aimed to test if the null hypothesis should be rejected in favour of the alternative hypothesis. The basic logic of a hypothesis test is to compare two statistical data sets. One data set is obtained by sampling, and the other originates from an idealized model. The null hypothesis is not rejected if the sample data is consistent with the idealized model. If the sample data is inconsistent with the idealized model and thus supports an alternative hypothesis, the null hypothesis is rejected in favour of the alternative hypothesis.

The criterion for rejecting the null hypothesis involves a so-called test statistic. The test statistic is a number calculated from the data set, obtained by measurements and observations, or more generally, by sampling.

**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.*