Perform Hypothesis testing with NumPy and SciPy's Stats module

Last update on March 27 2025 09:14:16 (UTC/GMT +8 hours)

7. Hypothesis Testing with SciPy Stats

Write a NumPy program to generate random samples and perform a hypothesis test using SciPy's stats module.

Sample Solution:

Python Code:

# Import necessary libraries
import numpy as np
from scipy import stats

# Generate two sets of random samples using NumPy
np.random.seed(0)  # For reproducibility
sample1 = np.random.normal(loc=5, scale=2, size=100)
sample2 = np.random.normal(loc=5.5, scale=2, size=100)

# Perform an independent t-test using SciPy's stats module
t_stat, p_value = stats.ttest_ind(sample1, sample2)

# Print the results of the hypothesis test
print("T-statistic:", t_stat)
print("P-value:", p_value)

Output:

T-statistic: -1.8750754551177837
P-value: 0.06225454602309727

Explanation:

• Import necessary libraries:
• Import NumPy for generating random samples and SciPy's stats module for hypothesis testing.
• Generate two sets of random samples using NumPy:
• Create two normally distributed samples with different means (loc) but the same standard deviation (scale).
• Perform an independent t-test using SciPy's stats module:
• Use ttest_ind to perform a t-test to compare the means of the two samples.
• Print the results of the hypothesis test:
• Display the t-statistic and p-value to determine if there is a significant difference between the two samples.

For more Practice: Solve these Related Problems:

• Write a Numpy program to generate two independent samples and perform a t-test using SciPy's stats.ttest_ind.
• Write a Numpy program to compare the variances of two datasets using SciPy's F-test implementation.
• Write a Numpy program to conduct a chi-square goodness-of-fit test on a categorical dataset using SciPy.
• Write a Numpy program to perform a paired t-test on related samples and interpret the p-value with SciPy's stats module.

Python-Numpy Code Editor:

Previous: Fit a curve to sample data using NumPy and SciPy's curve_fit.
Next: Ordinary differential equations with NumPy and SciPy.