Hypothesis Testing: A Different Perspective

This lecture explores a different perspective on hypothesis testing, focusing on the p-value and Neyman-Pearson framework. It discusses the significance level, decision rules, and optimal test statistics. The instructor explains the concept of family of test functions and decision rules, emphasizing the smallest significance level for rejecting the null hypothesis. Examples and applications of hypothesis testing are presented, including the interpretation of p-values and confidence intervals. The lecture also covers point estimation, hypothesis tests, and interval estimation, providing insights into variability and confidence levels.