Hypothesis Testing
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_1sampt_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_tukeyhsdtukey_results = pairwise_tukeyhsd(data, labels, alpha=significance_level)
