Which sampling method ensures that each subgroup is accurately represented in the sample?

Stratified random sampling is a method that ensures each subgroup, or stratum, of a population is accurately represented in the sample. This approach involves dividing the entire population into distinct subgroups based on specific characteristics, such as age, income level, or education. Once the population is divided, a random sample is taken from each stratum. This technique helps to ensure that all relevant characteristics of the population are represented in the sample, which enhances the validity and reliability of the research findings.

By utilizing stratified random sampling, researchers can obtain a more comprehensive view of the population and avoid biases that may arise if certain subgroups are over-represented or under-represented in the sample. This method is particularly useful when there are significant differences between the subgroups, as it allows for a more nuanced analysis of the data collected. In fields like marketing research, having accurate representation across various segments can lead to better insights and tailored strategies that resonate with different groups within the target market.