Question: difference between stratified random sampling and cluster sampling

title: Cluster sampling
text: The main difference between cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so sampling is done on a population of clusters ( at least in the first stage ) . In stratified sampling , the sampling is done on elements within each strata . In stratified sampling , a random sample is drawn from each of the strata , whereas in cluster sampling only the selected clusters are sampled . A common motivation of cluster sampling is to reduce costs by increasing sampling efficiency . This contrasts with stratified sampling where the motivation is to increase precision . 

Answer: answer: The main difference between cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so sampling is done on a population of clusters ( at least in the first stage ) . In stratified sampling , a random sample is drawn from each of the strata , whereas in cluster sampling only the selected clusters are sampled . A common motivation of cluster sampling is to reduce costs by increasing sampling efficiency, but the motivation of stratified sampling is to increase precision.
answer: The main difference between cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so sampling is done on a population of clusters. In stratified sampling , the sampling is done randomly on elements within each strata, whereas in cluster sampling only the selected clusters are sampled .
A motivation of cluster sampling is to reduce costs by increasing sampling efficiency, while the motivation in stratified sampling is to increase precision.