Performing hypothesis tests using SciPy's ttest_ind and chi2_contingency functions

Performing hypothesis tests is a fundamental step in statistical analysis to determine whether a sample is statistically significant or just a random variation. 

SciPy's ttest_ind and chi2_contingency functions are two commonly used statistical tests in Python for hypothesis testing. The ttest_ind function performs a two-sample t-test to determine whether two independent samples have different means. The chi2_contingency function performs a chi-squared test of independence to determine whether two categorical variables are independent of each other or not. 

In the given code, the program imports pandas as pd to create a DataFrame containing the number of smokers and patients. Then it uses the proportions_chisquare function from statsmodels.stats.proportion to perform a chi-squared test for the proportions of smokers and patients. The test results are stored in the variables chi2 and p. Similarly, and it uses the chi2_contingency function from scipy.stats to perform a chi-squared test for the independence of the smokers and patients. The results of this test are also stored in the variables chi2 and p. 

By performing hypothesis tests using these functions, we can determine whether the differences observed between samples are significant or just due to random chance. This can help us make better-informed decisions and draw more accurate conclusions from our data. 

Preview of the output that you will get on running this code from your IDE

Code

Instructions

To execute this code in VSCode, you can follow these steps:

1. Install Python and the PyTorch library on your desktop.
2. Open VSCode and create a new Python file in the editor.
3. Copy the code snippet and paste it into your file in VSCode. Make sure you print the chi2 value and p value using print(chi2) ,print(p) in the code
4. Save the file with a meaningful name and the appropriate file extension (.py for Python).
5. Open the VSCode terminal and navigate to the directory where your file is saved.
6. Run the command "python filename.py" to execute the code and see the output in the terminal.

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Environment Tested

Tested this solution in the following versions. Be mindful of changes when working with other versions.

• Visual Studio Code Version 1.76.0
• pandas Version 1.3.5
• statsmodels Version 0.13.5
• Scipy Version 1.7.3

By performing hypothesis tests using these functions, we can determine whether the differences observed between samples are significant or just due to random chance. This process also facilities an easy-to-use, hassle-free method to create a hands-on working version of code which would help us to do Performing hypothesis tests.

Dependent Library

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