Statistical Hypothesis Testing

Overview

Hypothesis testing provides a framework for making data-driven decisions by testing whether observed differences are statistically significant or due to chance.

Testing Framework

• Null Hypothesis (H0): No effect or difference exists
• Alternative Hypothesis (H1): Effect or difference exists
• Significance Level (α): Threshold for rejecting H0 (typically 0.05)
• P-value: Probability of observing data if H0 is true

Common Tests

• T-test: Compare means between two groups
• ANOVA: Compare means across multiple groups
• Chi-square: Test independence of categorical variables
• Mann-Whitney U: Non-parametric alternative to t-test