Which sampling method involves dividing the population into groups ($\mathrm{strata}$) and then taking a random sample from each group?

• A. Cluster sampling
• B. Simple random sampling
• C. Systematic sampling
• D. Stratified sampling

Answer: (D)

Verified step by step guidance

1. Understand the definition of each sampling method: Cluster sampling involves dividing the population into clusters and then randomly selecting entire clusters; Simple random sampling means every individual has an equal chance of being selected; Systematic sampling involves selecting every k-th individual from a list; Stratified sampling involves dividing the population into distinct groups called strata based on a characteristic.
2. Recognize that the key feature of stratified sampling is to first divide the population into strata, which are homogeneous groups, to ensure representation from each subgroup.
3. After dividing the population into strata, a random sample is taken independently from each stratum, which helps improve the precision of the overall sample by capturing variability within each group.
4. Compare this to cluster sampling, where entire clusters are selected rather than samples from each group, highlighting the difference in approach.
5. Conclude that the sampling method described—dividing the population into groups (strata) and then taking a random sample from each group—is called stratified sampling.