# Hypothesis Testing

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### 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)``````

Skill Path

### Analyze Data with Python

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