When working with data, particularly in machine learning, we frequently need to make decisions or validate assumptions based on samples. Is a new model significantly better than an old one? Does a specific feature have a measurable impact? Hypothesis testing provides a structured statistical framework to answer such questions.

This chapter covers the fundamentals of hypothesis testing. You will learn how to:

• Formulate the null hypothesis ($H_0$) and the alternative hypothesis ($H_1$).
• Understand the trade-offs between Type I ($\alpha$) and Type II ($\beta$) errors.
• Interpret p-values to make statistical decisions.
• Apply common tests, including t-tests for comparing means and Chi-squared tests for categorical data analysis.
• Get an overview of Analysis of Variance (ANOVA) for comparing multiple groups.

We will also demonstrate how to implement these tests efficiently using Python's SciPy library, providing practical tools for model evaluation and data analysis.