Describe cluster sampling.

Cluster sampling is a sampling technique where the population is divided into clusters, and a random sample of clusters is selected for analysis.

Cluster sampling is a method of sampling that is commonly used when the population is too large or too spread out to sample every individual. Instead, the population is divided into clusters, which are groups of individuals that are similar in some way. For example, a cluster might be a group of people who live in the same neighbourhood or attend the same school.

Once the clusters have been identified, a random sample of clusters is selected for analysis. This can be done using a variety of methods, such as simple random sampling or systematic sampling. Once the clusters have been selected, all individuals within the selected clusters are included in the sample.

Cluster sampling can be more efficient than other sampling methods, as it allows researchers to sample a large population without having to sample every individual. However, it can also be less accurate, as there may be more variation within clusters than between clusters. To account for this, researchers may choose to sample more clusters or increase the size of the sample within each cluster.

Overall, cluster sampling is a useful technique for sampling large populations, but it is important to carefully consider the size and composition of the clusters to ensure that the sample is representative of the population as a whole.