Statistical Hypothesis Testing: Inference and Interpretation

This lecture covers the fundamentals of statistical hypothesis testing, focusing on the process of making inferences about population parameters based on sample data. Topics include constructing confidence intervals, hypothesis testing procedures, interpreting p-values, and understanding the significance of test results. Through examples involving radon levels and coin toss experiments, the instructor illustrates how to formulate null and alternative hypotheses, compute test statistics, and make decisions based on significance levels. The lecture also addresses common pitfalls in hypothesis testing, such as interpreting results from non-random samples and distinguishing between statistical and practical significance.