Cluster sampling
Cluster sampling is a sampling technique used in statistics and market research. It involves dividing a population into clusters or groups, then randomly selecting some of these clusters to sample from. This method is often more practical and cost-effective than other sampling techniques, especially when the population is large and spread out.
For example, imagine a researcher conducting a survey on the eating habits of people in a large city. Instead of trying to survey every single person in the city, the researcher could divide the city into neighborhoods (clusters) and randomly select a few neighborhoods to survey. This way, they can still get a representative sample of the population without having to survey everyone.
Cluster sampling can be useful in situations where it is difficult or impractical to sample individuals directly. It can also help reduce costs and save time, making it a popular choice in many research studies.
One important consideration when using cluster sampling is ensuring that the clusters are truly representative of the population. If the clusters are not selected randomly or if they do not accurately reflect the overall population, the results of the study may be biased.
Overall, cluster sampling is a valuable tool in the field of statistics and research, offering a practical and efficient way to gather data from large and diverse populations.
For more information on cluster sampling, you can visit Wikipedia.