How to Conduct a Paired Samples T-Test in Python

How to Conduct a Paired Samples T-Test in Python

A paired samples t-test is used to compare the means of two related groups. The groups are "paired" because they are somehow related or matched.

For example, you might use a paired samples t-test to determine whether there's a significant difference in test scores between students' pre-test and post-test scores after undergoing some training.

To conduct a paired samples t-test in Python, you can use the scipy.stats module:

Step-by-Step Procedure:

  • Import necessary libraries:
import numpy as np from scipy import stats
  • Define your data:

Suppose we have the pre-test and post-test scores of 5 students:

pre_test_scores = np.array([25, 30, 28, 35, 28]) post_test_scores = np.array([30, 29, 30, 37, 31])
  • Conduct the t-test:
t_stat, p_val = stats.ttest_rel(post_test_scores, pre_test_scores)
  • Interpret the results:
alpha = 0.05 if p_val < alpha: print(f"Significant difference between the means (p={p_val:.3f})") else: print(f"No significant difference between the means (p={p_val:.3f})")

Here's the whole process combined:

import numpy as np from scipy import stats pre_test_scores = np.array([25, 30, 28, 35, 28]) post_test_scores = np.array([30, 29, 30, 37, 31]) t_stat, p_val = stats.ttest_rel(post_test_scores, pre_test_scores) alpha = 0.05 if p_val < alpha: print(f"Significant difference between the means (p={p_val:.3f})") else: print(f"No significant difference between the means (p={p_val:.3f})")

After running the code, you'll get a message indicating whether there's a significant difference between the pre-test and post-test scores based on the p-value and your chosen significance level (alpha).

Note: Ensure that you've installed the required libraries (e.g., numpy and scipy) before executing the code. You can install them using pip:

pip install numpy scipy

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