Final answer:
Stratified sampling is a method where a population is divided into homogeneous groups called strata, and random samples are drawn from each to ensure representation and minimize bias.
Step-by-step explanation:
The process of stratified sampling is used to ensure that subgroups within a larger population are adequately represented. To conduct a stratified sample, the population is divided into homogeneous groups known as strata, and a random sample is then selected from each stratum. The purpose of this approach is to minimize bias and ensure different strata are represented, which cannot be achieved through random data collection alone.
For instance, consider an educational researcher who wants to understand teaching strategies across genders. They might use stratified sampling by dividing the population into two strata: male and female teachers. Then, they could randomly select an equal number of teachers from each group to ensure that both are represented.
Different types of samplings such as cluster sampling, systematic sampling, and convenience sampling serve distinct purposes and have varying degrees of ability to minimize bias and represent different strata within the population.