Published Oct 26, 2023 Stratified random sampling is a sampling technique where the population is divided into distinct subgroups, or strata, based on certain characteristics. The subgroups are created to ensure that each subgroup is internally homogeneous but different from the other subgroups. Then, a random sample is taken from each stratum, which forms the overall sample for the study or survey. Imagine a study that aims to examine the average income of residents in a city. The city’s population consists of different age groups, ranging from young adults to senior citizens. To ensure a representative sample, the study uses stratified random sampling. The population is divided into distinct age groups or strata, for example, 18-25 years old, 26-40 years old, 41-60 years old, and 61 years and above. From each stratum, a random sample of individuals is selected. This approach guarantees that the sample reflects the population’s age distribution accurately. By using stratified random sampling, the study can make accurate inferences about the average income of residents in each age group and the overall population. Stratified random sampling is a crucial sampling technique that ensures the representativeness of a sample. By dividing the population into distinct strata, researchers can capture the diversity within the population while maintaining a balanced representation of each subgroup. This sampling method is particularly useful when the population has significant variations or differences in certain characteristics. It allows researchers to draw conclusions not just for the overall population but also for specific subgroups. Moreover, stratified random sampling helps reduce sampling errors and improve the accuracy of the study’s findings. It enhances the generalizability of the results to the entire population, making it an essential tool in research, market analysis, and decision-making processes.Definition of Stratified Random Sampling
Example
Why Stratified Random Sampling Matters
Economics