Explain and interpret p-values correctly

## Context You are evaluating a change to a fraud decision rule (e.g., a new threshold or step-up authentication rule). You run an experiment comparing **Control** vs **Treatment**. ## Questions 1. What is a **p-value**? State the definition precisely. 2. What does a p-value **not** tell you (common misinterpretations)? 3. How do **sample size**, **effect size**, and **variance** influence p-values? 4. How would you handle: - **Multiple testing** (many metrics or many segments like regions) - **Peeking** (checking results daily and stopping early) - **Imbalanced outcomes** and **delayed labels** (e.g., chargebacks arriving weeks later) 5. What would you report alongside the p-value to make a decision in a fraud setting (confidence intervals, practical significance, cost-based impact, etc.)?