Explain the difference between a stratified random sample and cluster random sample
What will be an ideal response?
First, both sampling techniques are examples of statistical sampling procedures. However, they are very different in purpose. In stratified random sampling, the population is broken down into homogeneous groups called strata. The idea is that the elements in each strata are as much alike as possible so that the required sample size from each strata is reduced. Then the total sample size selected from all strata will hopefully be less than the required sample size that would be needed if stratification were not used. In cluster sampling, the population is divided into geographical subgroups. The hope is that each subgroup is a mirror image of the population as a whole. Then a few of the clusters are randomly selected and all sampling of individual items is taken from only the selected clusters. The objective is to reduce the cost of sampling by reducing the physical area that must be covered in the sample.
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What will be an ideal response?