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)