A SAS analyst uses PROC SURVEYSELECT to create a stratified random sample. Why is stratified sampling preferred over simple random sampling when the population contains small but important subgroups?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because stratified random sampling divides the population into strata and samples independently within each stratum, guaranteeing minimum representation for each group. Rare subgroups could have zero or very few observations under simple random sampling, making estimates for them unreliable.
Full explanation below image
Full Explanation
B is correct because stratified random sampling divides the population into strata and samples independently within each stratum, guaranteeing minimum representation for each group. Rare subgroups could have zero or very few observations under simple random sampling, making estimates for them unreliable. A is wrong; speed is not the advantage. C is wrong; stratified sampling may still require design-based weighting in analysis. D is wrong; stratification ensures stratum representation but does not guarantee exact population mean matching for all variables.