What is stratified sampling?

What is stratified sampling?

• A. Dividing the population into homogeneous strata and sampling within each to improve precision.
• B. Sampling only from one subgroup to save costs.
• C. Non-probability sampling of convenience.
• D. Dividing the population into heterogeneous groups and sampling within each.

Answer: (A)

Stratified sampling groups the population into homogeneous subgroups, or strata, based on characteristics related to the study, and then samples from each stratum. By making members within a stratum similar to each other, the variation inside each group is smaller, which reduces the overall sampling error. After collecting samples from all strata, the data are combined to produce an overall estimate that better represents the whole population. This method also helps ensure that important subgroups are represented in the sample, which is especially helpful if the aspect being studied varies across groups. You can sample proportionally to each stratum’s size or allocate samples differently depending on the study goals. Choosing only one subgroup would bias results and miss variation across the population. Non-probability, convenience sampling does not guarantee representativeness. Dividing into heterogeneous groups and sampling within each would not exploit the precision benefits of stratification and would fail to reduce variance as effectively.

Stratified sampling groups the population into homogeneous subgroups, or strata, based on characteristics related to the study, and then samples from each stratum. By making members within a stratum similar to each other, the variation inside each group is smaller, which reduces the overall sampling error. After collecting samples from all strata, the data are combined to produce an overall estimate that better represents the whole population. This method also helps ensure that important subgroups are represented in the sample, which is especially helpful if the aspect being studied varies across groups. You can sample proportionally to each stratum’s size or allocate samples differently depending on the study goals.

Choosing only one subgroup would bias results and miss variation across the population. Non-probability, convenience sampling does not guarantee representativeness. Dividing into heterogeneous groups and sampling within each would not exploit the precision benefits of stratification and would fail to reduce variance as effectively.