Cluster and Stratified Sampling
Explain the differences between sampling methods? What factors should be considered when selecting sampling strategies? + Example?
Sample Solution
Cluster and Stratified Sampling Sampling is a process used in statistical analysis in which a predetermined number of observations are taken from a larger population. Cluster sampling is a sampling plan used when mutually homogeneous yet internally heterogeneous groupings are evident in a statistical population. Stratified sampling is a method of sampling from a population which can be partitioned into sub-populations. The main differences 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 final stage). In stratified sampling, the sampling is done on elements within each stratum. In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling only 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.
stores. However this goes against every value that makes John Lewis what it is today and as such consumers may not be so fond of the move however the basic range brought out in Waitrose called their “essential range” have sold over £100 million of products since there release indicating a changing market for John Lewis showing a very lucrative opportunity for John Lewis.
John Lewis online could also be a potential move, as mentioned having an online store would cost considerably less while at the same time giving the consumer the chance to shop from anywhere at anytime they wish. Furthermore discounts could be offered as there are many less overheads involved in the running of the store.