Statistical Inference: Approximate Critical Values and Confidence Intervals

This lecture covers the theoretical background for statistical inference, focusing on the construction of confidence intervals and the use of approximate critical values. The instructor explains the concept of pivots and approximate pivots, illustrating their application in various statistical scenarios. Key topics include Wald tests, Slutsky's theorem, interpretation of confidence intervals, and the importance of understanding the probabilistic nature of intervals. Through examples and theoretical explanations, students will gain insights into the construction and interpretation of confidence intervals in statistical inference.