Review:
Cluster Sampling
overall review score: 4.2
⭐⭐⭐⭐⭐
score is between 0 and 5
Cluster sampling is a sampling technique used in statistics for selecting a group of subjects that are already in close proximity to each other, rather than selecting them individually at random.
Key Features
- Selecting groups or clusters of subjects
- Useful for large populations
- Reduced costs compared to individual sampling
- Allows for stratified sampling within clusters
Pros
- Efficient for large populations
- Cost-effective compared to individual sampling
- Allows for easy access to clusters of subjects
Cons
- May introduce bias if clusters are not representative of the population
- Requires careful selection of clusters to ensure accuracy