In which sampling method are individuals divided into subgroups based on characteristics, and then random samples are selected from each subgroup?

Stratified sampling is the method in which individuals are divided into subgroups known as strata, based on specific characteristics such as age, income level, or education level. After forming these subgroups, random samples are drawn from each stratum. This technique is particularly useful when researchers want to ensure that the sample reflects certain characteristics of the population, enhancing the representativeness of the sample and improving the accuracy of the estimates. By sampling from each subgroup, stratified sampling helps to reduce sampling bias and can lead to more precise results compared to methods that do not consider the characteristics of the population.

In different sampling methods such as systematic sampling, a fixed interval is used to select samples (e.g., selecting every 10th individual). In cluster sampling, entire groups or clusters are selected at once rather than individuals from subgroups, which can lead to less precision if the clusters are not homogeneous. Simple random sampling involves selecting individuals from the population entirely at random without consideration of any specific characteristics, which may not account for the diversity present in the population.