Lecture
This lecture by the instructor covers the concept of hypothesis testing in statistics, where the true parameter is assumed to lie in one of two subsets, and the sample data is used to decide between these possibilities. The lecture delves into the null and alternative hypotheses, illustrated with examples like the search for the Higgs boson. It explains the role of test functions in making decisions based on samples and discusses the potential errors that can occur in hypothesis testing. The Neyman-Pearson framework is also presented, emphasizing the importance of controlling Type I and Type II error probabilities.