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Cluster vs Stratified Sampling

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Cluster Sampling is a sampling method where the population (or sampling frame) is divided into sub-populations called clusters, generally according to geographical differences or naturally occurring divisions. Instead of sampling individuals, entire subgroups are randomly selected.


Stratified Sampling is a sampling method where the population (or sampling frame) is divided into sub-populations or strata, according to some common characteristic. A simple random sample is selected from each strata for sampling.


An application-oriented question on the topic along with responses can be seen below. The best answer was provided by Glory Gerald on 27th Nov 2020.


Applause for all the respondents - Glory Gerald, Tina Misra


Q 316. The population is divided into smaller parts in both cluster and stratified sampling. However, both are different. How is Cluster sampling different from Stratified sampling? Explain with examples.



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It is important to have an unbiased sample concerning the population when conducting surveys so that the results and predictions made are more accurate. This may not be the case of random sampling as here the samples are always biased as it does not represent the population accurately. Hence, Stratified sampling and Cluster Sampling are preferred to overcome the bias and efficiency issues of simple random sampling.


Difference between Stratified and Cluster Sampling:





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Both are random sampling.

Sratified allows you to pick basis a specific criterion eg pick 10 girls ans 10 boys randomly from the total group.

Cluster may have eg several districts having large population are clusters and lets say out of 50 districts you chose 5 randomly out of that 50 is clustered sampling


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