4 Hypothesis Testing in Python: Step-by-Step Guide for Data Science
Learn the fundamentals of hypothesis testing using Python in this step-by-step tutorial. This video covers the essential concepts, from stating hypotheses and choosing significance levels to selecting the right statistical test for your data. You will see practical examples using t tests and nonparametric alternatives, along with tips for checking assumptions and interpreting results.
Follow along with hands-on coding exercises and a mini project to build your skills. By the end, you will understand how to apply hypothesis testing in real-world scenarios and avoid common mistakes. Perfect for beginners in data science and statistics.
00:00 Introduction
00:22 Importing libraries
00:51 What is hypothesis testing
01:30 Creating sample data
01:59 Stating hypotheses
02:45 Exploring sample data
03:09 Choosing significance level
03:59 Selecting the test
04:25 Performing the t test
04:52 Making decisions with p values
05:21 Checking assumptions
05:45 Testing for normality
06:13 Adjusting significance level
06:47 Repeating the test with new alpha
07:14 Nonparametric tests overview
07:39 Using Mann Whitney U test
08:04 Mini project challenge
08:41 Example solution for mini project
09:37 Troubleshooting mistakes
10:09 Coding tips and best practices
10:32 Summary and final thoughts
#Python #Statistics #DataScience
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