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Detailed explanation-1: -What is stratified sampling? In stratified sampling, researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment). Once divided, each subgroup is randomly sampled using another probability sampling method.

Detailed explanation-2: -Cluster sampling is a method of probability sampling where researchers divide a large population up into smaller groups known as clusters, and then select randomly among the clusters to form a sample. Key Terms.

Detailed explanation-3: -Stratified sampling In this method, the population is first divided into subgroups (or strata) who all share a similar characteristic.

Detailed explanation-4: -Description: Stratified sampling is a common sampling technique used by researchers when trying to draw conclusions from different sub-groups or strata. The strata or sub-groups should be different and the data should not overlap. While using stratified sampling, the researcher should use simple probability sampling.

Detailed explanation-5: -Stratified sampling is a method of obtaining a representative sample from a population that researchers have divided into relatively similar subpopulations (strata). Researchers use stratified sampling to ensure specific subgroups are present in their sample.