What is the primary method of cluster sampling?

The primary method of cluster sampling involves dividing the entire population into distinct groups, known as clusters, and then randomly selecting entire clusters to form the sample. This approach is particularly useful when dealing with a large population spread over a wide geographic area, as it can save time and resources while still providing a representative sample.

In this method, the clusters are typically defined based on natural groupings within the population, such as geographic locations or other relevant factors. After selecting the clusters, researchers can then include all individuals within those chosen clusters or randomly select individuals from them for the final sample. This process helps to ensure that the sample reflects the diversity within the population while simplifying the sampling process.

The other methods listed do not accurately represent the essence of cluster sampling, as options that involve individual selection or focusing solely on the largest groups deviate from the random selection of clusters.