What is a key difference between stratified sampling and simple random sampling?

• A. Stratified sampling ensures representation from subgroups, while simple random sampling does not guarantee representation.
• B. Stratified sampling is always faster.
• C. Simple random sampling requires clusters.
• D. Simple random sampling is identical to cluster sampling.

Answer: (A)

Stratified sampling focuses on representing important subgroups by dividing the population into strata and sampling from each stratum. This approach helps ensure the sample includes members from every subgroup in proportion to their presence in the population, which reduces sampling error for subgroup-related analyses. Simple random sampling, by contrast, draws from the entire population with equal chance but does not guarantee that subgroups will be represented. So the main idea is that stratified sampling guarantees subgroup representation, while simple random sampling does not. The other statements aren’t accurate: stratified sampling isn’t inherently faster, simple random sampling doesn’t require clusters, and simple random sampling is not identical to cluster sampling.