Cluster sampling is useful when it would be impossible or impractical to identify every person in the sample. Suppose a college does not print a student directory. It would be most practical in this instance to sample students from classes. Rather than randomly sample 10% of students from each class, which would be a difficult task, randomly sampling every student in 10% of the classes would be easier.

Sampling every student in a class is not a random procedure. However, by randomly selecting the classes, you have a greater probability of capturing a representative sample of the population. Many students believe that it is not possible to gather a representative sample for a class project or a thesis. However, this type of cluster sampling is easily done, especially since all colleges publish lists of classes for registration.