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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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scipy/scipy.stats.fisher_exact/README.md
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@ -22,7 +22,8 @@ scipy.stats.fisher_exact(table, alternative='two-sided')
>
> 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.
> Parameters:
>
> **alternative** : {‘two-sided’, ‘less’, ‘greater’}, optional
> Defines the alternative hypothesis. The following options are available (default is ‘two-sided’):
>
@ -30,4 +31,41 @@ scipy.stats.fisher_exact(table, alternative='two-sided')
> * ‘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.
> Returns:
>
> **res** : SignificanceResult
>
> An object containing attributes:
>
> **statistic** : float
>
> This is the prior odds ratio, not a posterior estimate.
>
> **pvalue** : float
>
> The probability under the null hypothesis of obtaining a table at least as extreme as the one that was actually observed.
The input table is [[a, b], [c, d]]. Where N_A = a + c for the elements in group A and N_B = b + d for the elements in group B.
[[N_A-c, N_B-d], [c, d]]
## [Example](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html)
||Group A|Group B|
|---|---|---|
|Yes| 7 | 17 |
|No| 15| 5|
This translates in to the table: [[7, 17], [15, 5]]
```python
from scipy.stats import fisher_exact
res = fisher_exact([[7, 17], [15, 5]], alternative="less")
print(res.statistic) # -> 0.13725490196078433
print(res.pvalue) # -> 0.0028841933752349743
```