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.