Hypothesis Testing: The Statistical Framework for Data Science Decisions

Hypothesis testing provides a rigorous mathematical framework to distinguish signal from noise in data. It covers Z-test for large samples with known variance, T-test for real-world scenarios with unknown variance, and Chi-Square test for categorical relationships. These tests help data scientists make evidence-based decisions in A/B testing, clinical trials, and business analytics.