Two-Sample T-Test with Statsmodels
Statsmodels is a python library that is specifically used to compute different statistical models and for conducting statistical tests. This library makes use of R-style modules and dataframes.
Firstly, let’s create the sample data. We are creating two arrays and now let’s perform the two-sample T-test. Statsmodels library provides ttest_ind() function to conduct two-sample T-Test whose syntax is given below,
Syntax: ttest_ind(data_group1, data_group2)
Here,
- data_group1: First data group
- data_group2: Second data group
Example:
Python3
# Python program to conduct# two-sample t-test using statsmodels# Importing libraryfrom statsmodels.stats.weightstats import ttest_indimport numpy as npimport pingouin as pg# Creating data groupsdata_group1 = np.array([160, 150, 160, 156.12, 163.24, 160.56, 168.56, 174.12, 167.123, 165.12])data_group2 = np.array([157.97, 146, 140.2, 170.15, 167.34, 176.123, 162.35, 159.123, 169.43, 148.123])# Conducting two-sample ttestttest_ind(data_group1, data_group2) |
Output:
Two-Sample T-Test with Statsmodels
Interpreting the result:
This is the time to analyze the result. The p-value of the test comes out to be equal to 0.521, which is greater than the significance level alpha (that is, 0.05). This implies that we can say that the average height of students in one class is statistically not different from the average height of students in another class.
How to Conduct a Two Sample T-Test in Python
In this article, we are going to see how to conduct a two-sample T-test in Python.
This test has another name as the independent samples t-test. It is basically used to check whether the unknown population means of given pair of groups are equal. tt allows one to test the null hypothesis that the means of two groups are equal
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