Population is divided into subgroups based on similar characteristics and a simple random sample is drawn from each subgroup.

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

Answer: (A)

Dividing the population into homogeneous subgroups and drawing a simple random sample from each is stratified random sampling. The idea is that each subgroup, or stratum, contains similar members with respect to the trait you care about, so sampling within each stratum gives more precise overall estimates and ensures representation of every part of the population. Because you perform random sampling inside every subgroup, you maintain randomness while reducing variability that comes from differences between groups.

This approach differs from systematic random sampling, which selects units at a fixed interval from a list without forming strata; from cluster sampling, which groups the population into clusters and samples entire clusters (or samples within only some clusters) rather than from every subgroup; and from quota sampling, which uses non-random selection to meet predefined subgroup quotas.