Stratified Sampling

Stratified Sampling is a sampling technique in which the population is divided into homogeneous subgroups called strata, and a random sample is taken from each stratum. This technique ensures that each stratum is represented in the sample, which helps to reduce sampling error and increase the accuracy of the results. Stratified Sampling is commonly used in Data Science to ensure that the sample is representative of the population, especially when the population is heterogeneous. It is particularly useful when the variable of interest is known to be related to a certain characteristic of the population, such as age, gender, or income. By stratifying the population based on this characteristic, the sample can be selected in a way that ensures that each subgroup is represented in the sample in proportion to its size in the population.