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+# Fisher Exact Test
+{:.no_toc}
+
+<nav markdown="1" class="toc-class">
+* TOC
+{:toc}
+</nav>
+
+## Top
+
+
+Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
+
+
+## [scipy.stats.fisher_exact](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html)
+
+```python
+scipy.stats.fisher_exact(table, alternative='two-sided')
+```
+
+> Perform a Fisher exact test on a 2x2 contingency table.
+> 
+> The null hypothesis is that the true odds ratio of the populations underlying the observations is one, and the observations were sampled from these populations under a condition: the marginals of the resulting table must equal those of the observed table. The statistic returned is the unconditional maximum likelihood estimate of the odds ratio, and the p-value is the probability under the null hypothesis of obtaining a table at least as extreme as the one that was actually observed. There are other possible choices of statistic and two-sided p-value definition associated with Fisher’s exact test; please see the Notes for more information.
+
+
+> **alternative** : {‘two-sided’, ‘less’, ‘greater’}, optional
+> Defines the alternative hypothesis. The following options are available (default is ‘two-sided’):
+> 
+> * ‘two-sided’: the odds ratio of the underlying population is not one
+> * ‘less’: the odds ratio of the underlying population is less than one
+> * ‘greater’: the odds ratio of the underlying population is greater than one
+
+See the Notes for more details.