Which sampling method draws a sample from a specified sub-group of the population and selects randomly from each stratum?

Stratified sampling is a method used to ensure that specific sub-groups within a population are represented in the sample. In this approach, the population is divided into different strata or segments based on shared characteristics, such as age, income, or education level. Once the strata are identified, random samples are drawn from each of these groups.

This technique allows researchers to obtain a more accurate representation of the overall population by ensuring that each subgroup is represented according to its proportion in the total population. Stratified sampling is particularly useful in studies where certain characteristics of the sub-groups are crucial to the research outcomes, enhancing the reliability and validity of the findings.

In contrast, the other sampling methods mentioned do not include this stratification process. Simplified sampling is not a recognized term within statistical sampling methodologies. Cluster sampling involves dividing the population into clusters and randomly selecting entire clusters rather than individual members within strata. Random sampling entails selecting individuals from the entire population without any consideration for sub-group characteristics, which could lead to underrepresentation of some strata. Thus, stratified sampling is distinct and specifically addresses the need for balanced representation across various segments of a population.