Society of Actuaries (SOA) PA Practice Exam 2025 – 400 Free Practice Questions to Pass the Exam

Stratified Random Sampling is an important technique in statistics used to ensure that a sample accurately represents the various sub-populations within a larger population. The primary purpose of this method is to divide the population into distinct sub-groups, known as strata, based on shared characteristics such as age, gender, socioeconomic status, or other relevant criteria.

By doing this, the sampling process can then take a random sample from each stratum, ensuring that all relevant sub-groups are represented in the final sample in proportion to their presence in the overall population. This enhances the precision and reliability of estimates made from the sample, as it accounts for the heterogeneity within the population. Each stratum can vary significantly from one another, and sampling randomly from these groups ensures that the sample better reflects the complexities of the entire population.

This approach contrasts with other sampling methods that may not consider these factors, potentially leading to biased or unrepresentative samples. Thus, stratified sampling is particularly useful when researchers want to ensure that key characteristics are represented in the study in a structured and systematic way.