What characterizes stratified sampling?

What characterizes stratified sampling?

• A. Population divided into clusters; samples drawn from each cluster
• B. Population divided into strata; samples drawn from each stratum
• C. Every member has an equal chance of selection
• D. Participants self-select or volunteer

Answer: (B)

Stratified sampling divides the population into subgroups that are similar within themselves and different from other groups, called strata, based on a relevant characteristic. Then you take samples from each strata, often in proportion to the strata’s size. The goal is to make sure every part of the population is represented in the final sample, which can reduce sampling error when the groups differ on the outcome of interest. This approach contrasts with other methods: clustering would sample whole clusters, not ensure representation from each subgroup; simple random sampling treats every individual the same chance but doesn’t guarantee every subgroup is represented; and voluntary or self-selected samples can introduce bias because not everyone has an equal opportunity to be included.

Stratified sampling divides the population into subgroups that are similar within themselves and different from other groups, called strata, based on a relevant characteristic. Then you take samples from each strata, often in proportion to the strata’s size. The goal is to make sure every part of the population is represented in the final sample, which can reduce sampling error when the groups differ on the outcome of interest. This approach contrasts with other methods: clustering would sample whole clusters, not ensure representation from each subgroup; simple random sampling treats every individual the same chance but doesn’t guarantee every subgroup is represented; and voluntary or self-selected samples can introduce bias because not everyone has an equal opportunity to be included.