Description: Stratified sampling is a sampling method that involves dividing a population into subgroups, known as strata, and then selecting samples from each of these strata. This approach is used to ensure that each subgroup is represented in the final sample, which can improve the accuracy and validity of the results. The main idea behind stratified sampling is that the population may be heterogeneous, and by dividing it into homogeneous strata, more precise estimates of the characteristics of interest can be obtained. This method is particularly useful when it is expected that variations within the strata are smaller than variations between them. Additionally, stratified sampling can be proportional, where the sample size from each stratum is proportional to the size of the stratum in the population, or disproportional, where different sample sizes are chosen for each stratum depending on the research. In the context of statistics and data analysis, stratified sampling is essential to ensure that models are trained and evaluated fairly and representatively, avoiding biases that could arise from a non-representative sample.
