Statistics: Hypothesis Testing & Confidence Intervals

This lecture covers the basics of statistics, focusing on hypothesis testing and confidence intervals. Starting with an overview of descriptive statistics, the instructor explains mean, variance, and normal distribution. The lecture delves into heavy-tailed distributions, robust statistics, and the importance of understanding data distributions. It then explores hypothesis testing, emphasizing the logic behind it and the significance of p-values. The instructor discusses the Bayesian approach as an alternative to hypothesis testing and introduces confidence intervals as a way to quantify uncertainty. The lecture concludes with a detailed explanation of multiple-hypothesis testing and family-wise error rate corrections.