Similarities Between Stratified and Cluster Sampling

Although cluster sampling and stratified sampling have certain differences, they also have some similarities:-

• Both techniques aim to increase sampling effectiveness by segmenting the population into smaller groups.

• Both approaches take into account population variability.

• Random sampling techniques are used in stratified and cluster sampling.

• When sampling the entire population is not viable or cost-effective, stratified and cluster sampling can also be useful alternatives.

The art of deducing information about large data and frequently challenging population from the analysis of a smaller sample is a the foundation of statical reasoning in the vast field of data analysis and research. This practice is referred to as “Sampling“. For any type of market research study, probability sampling is a method of choosing samples from a large population. The theory behind this is to randomly select a sample for the purpose of survey research. Stratified Sampling and Cluster Sampling are the two type of probability sampling.

Stratified and Cluster Sampling are statistical sampling techniques used to efficiently gather data from large populations. Stratified sampling divides the population into distinct subgroups based on characteristics or variables, ensuring homogeneity and variation. A random sample is selected from each stratum, reducing potential bias and ensuring accurate estimates. Cluster sampling divides the population into naturally occurring groups, such as geographical regions or organizational units, and randomly selects clusters to capture variability within them.

Both stratified and cluster sampling have several benefits, but they also have some drawbacks. We will see the difference between them in brief in this article.