Python for Data Science 11 test of mean difference - How to perform t-test

How to perform t-tests
📊 Understanding Mean Differences | 1-Sample, Independent & Paired t-Tests + Cohen’s d Effect Size

Are you analyzing differences in means and unsure which statistical test to use? In this step-by-step tutorial, you'll learn how to choose and apply the right t-test to your data — whether it's a one-sample test, a comparison between two groups, or a before-and-after study.

In this video, we cover:

✅ What mean differences represent in research
✅ When to use a One-Sample t-Test
✅ How to compare two independent groups with an Independent Samples t-Test (A/B testing)
✅ How to analyze repeated measures with a Paired Samples t-Test
✅ How to interpret Cohen’s d for effect size and practical significance
✅ How to test assumptions like normality and equal variance
✅ Real-world examples from business and medical research

Whether you’re a student, researcher, or data analyst, this video will help you build confidence in applying inferential statistics to real problems. 💡

🧪 Tools used: Python (Scipy, Statsmodels), clear visuals, and interpretation

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