Stratified Sampling: Theory and Applications

This lecture covers the concept of stratified sampling, where the population is divided into subgroups or strata to improve the accuracy of the sample. The instructor discusses the idea of stratification, random sampling within strata, and the calculation of stratified estimators. Various properties and formulas related to stratified sampling are explained, such as conditional density functions and variance calculations. The lecture also touches on proportional allocation and the process of choosing sample sizes within strata. Examples and practical applications of stratified sampling are presented to illustrate its importance in statistical analysis.