In stratified random sampling, what is the population divided into?

In stratified random sampling, the population is divided into subgroups, known as strata, which share similar characteristics. This method aims to ensure that each subgroup is adequately represented in the sample. By categorizing the population into these distinct strata—such as age, income level, or education—researchers increase the likelihood that the sample reflects the diversity of the total population.

This approach is particularly useful when researchers anticipate that different strata may exhibit different behaviors or responses. By sampling from each subgroup, research findings can be more robust and provide insights that might be missed if a simple random sample was taken from the entire population. In contrast, other sampling methods, such as cluster sampling, involve dividing the population into clusters or groups without guaranteeing that each subgroup is represented, which can lead to bias if certain characteristics are concentrated in particular clusters.