### Univariate T-test

A *univariate T-test* (or 1 Sample T-test) is a type of hypothesis test that compares a sample mean to a hypothetical population mean and determines the probability that the sample came from a distribution with the desired mean.

This can be performed in Python using the `ttest_1samp()`

function of the `SciPy`

library. The code block shows how to call `ttest_1samp()`

. It requires two inputs, a sample distribution of values and an expected mean and returns two outputs, the t-statistic and the p-value.

```
from scipy.stats import ttest_1samp
t_stat, p_val = ttest_1samp(example_distribution, expected_mean)
```

### Tukey’s Range Hypothesis Tests

A *Tukey’s Range* hypothesis test can be used to check if the relationship between two datasets is statistically significant.

The Tukey’s Range test can be performed in Python using the `StatsModels`

library function `pairwise_tukeyhsd()`

. The example code block shows how to call `pairwise_tukeyhsd()`

. It accepts a list of data, a list of labels, and the desired significance level.

```
from statsmodels.stats.multicomp import pairwise_tukeyhsd
tukey_results = pairwise_tukeyhsd(data, labels, alpha=significance_level)
```