Cluster sample
Cluster sampling is a sampling technique used in statistics where the population is divided into groups, or clusters, and a simple random sample of these clusters is selected. This method is often used when it is difficult or impractical to obtain a complete list of the population being studied.
For example, if a researcher wants to study the eating habits of students at a university, they may choose to use cluster sampling by dividing the university into different departments and then randomly selecting a few departments to survey. This allows the researcher to gather data from a representative sample of the population without having to survey every single student.
Cluster sampling is a useful technique for obtaining a representative sample of a large population while still being cost-effective and efficient. However, it is important to ensure that the clusters are homogenous and that each cluster is a fair representation of the overall population.
Advantages of cluster sampling: Cost-effective Time-efficient Allows for a representative sample of a large population
Overall, cluster sampling is a valuable tool in statistics for obtaining accurate and reliable data from large populations.
For more information on cluster sampling, you can visit the Wikipedia page here.