Cluster sampling is a widely used sampling method for marketing research where the population is large and geographically dispersed. For example, researchers want to know the career choice of high school students in a city. This would involve surveying students from every school in the city, which is a time-consuming and expensive process. Even a randomly selected sample would not be an adequate representation of this large and diverse population. Using cluster sampling, researchers divide the schools into different clusters and then randomly select some of the clusters to form the sample. Now, every student from those selected clusters is interviewed. Thus, the researchers narrowed down the large population into several smaller clusters and randomly selected some of the clusters for the experiment. Unlike cluster sampling, in stratified sampling, only a few individuals from each stratum are chosen. Also, in stratified sampling, each stratum is a homogeneous group, while in cluster sampling, the clusters are heterogeneous groups of individuals. Although this method is easier and cost-effective, samples drawn from cluster sampling are more prone to bias and high sampling error.