This lecture covers statistical models, point estimation, and the problem of inference. It discusses the concepts of sufficiency, ancillarity, and completeness in statistics, emphasizing the importance of sufficient and minimally sufficient statistics. The lecture also explores the Fisher-Neyman Factorization Theorem and Basu's Theorem, highlighting the role of complete statistics in data reduction and independence of statistics.