Stratified random sampling involves dividing a diverse population into smaller, similar groups based on a specific trait. Individuals are then randomly selected from each group using a random method.
This approach is beneficial in obtaining a more inclusive sample and enhancing the accuracy of data analysis.
For example, when studying student opinions on a topic, researchers may divide the population by grade level and randomly select participants from each group.
By doing so, stratified random sampling helps to address differences within the population and provides a more precise estimation of population parameters.
