Stratified sampling is a technique where the population is divided into distinct subgroups or "strata" based on a specific characteristic (such as age, income, or education level). Then, a random sample is drawn from each stratum. The purpose is to ensure that each subgroup is proportionally represented in the sample.
Where it is used:
When the population is heterogeneous: Stratified sampling is useful when the population consists of different subgroups that may have varying characteristics.
To improve precision: It reduces sampling error by ensuring each subgroup is adequately represented.
Common applications: Used in surveys, opinion polls, market research, and studies in which researchers want to ensure representation of certain characteristics like gender, age, or income level.
This method ensures that important subgroups are not underrepresented.
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