Answer: cluster sampling.

## Explanation This is an example of **cluster sampling** because: 1. **Cluster sampling** involves dividing the population into groups (clusters) and then randomly selecting some clusters to sample from. 2. In this scenario: - The population is all consumers in the country - The clusters are the cities (geographic groupings) - The researcher randomly selects 3 cities (clusters) - Within each selected city, a simple random sample of 10 consumers is taken **Key characteristics of cluster sampling:** - Population is divided into clusters - Clusters are randomly selected - All elements within selected clusters are sampled (or a random sample from each cluster) **Why not the other options:** - **B. Systematic sampling**: Involves selecting every kth element from a list after a random start. Not applicable here. - **C. Stratified random sampling**: Involves dividing the population into strata (subgroups with similar characteristics) and then taking random samples from each stratum. Here, cities are not strata but clusters. **Difference between stratified and cluster sampling:** - **Stratified**: Strata are homogeneous groups; sample from ALL strata - **Cluster**: Clusters are heterogeneous groups; sample from SOME clusters In this case, cities serve as naturally occurring clusters, making this a classic example of cluster sampling.

Author: LeetQuiz .