Understanding T-Test and Chi-Square Test: Key Concepts, Applications, and Practical Examples

Author(s): Ganesh Bajaj

Originally published on Towards AI.

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In the world of statistics, researchers and data analysts frequently face the challenge of determining whether observed differences in data are due to real effects or just random chance. To solve this problem, various statistical tests are employed to assess relationships, differences, and patterns in data. Two of the most commonly used tests in this regard are the T-Test and the Chi-Square Test.

Both tests serve different purposes. The T-Test is used when comparing numerical values (like averages or means), while the Chi-Square Test is used to analyze categorical data to determine associations between variables.

This article will cover both tests in detail, exploring how they work, when to use them, their formulas, limitations, and examples with Python code.

A t-test is a statistical hypothesis test that compares the means of two groups to determine if they are significantly different from each other. It’s commonly used when the sample sizes are small, and the data follows a normal distribution.

There are three main types of t-tests:

Independent (two-sample) t-test: Compares the means of two independent groups.Paired (dependent) t-test: Compares the means of the same group under different conditions (e.g., before and after treatment).One-sample t-test:… Read the full blog for free on Medium.

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Published via Towards AI