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Explain the difference between a stratified sample and a cluster sample. (Select all that apply).
1) In a stratified sample, every sample of size n has an equal chance of being included.
2) In a stratified sample, random samples from each strata are included.
3) In a cluster sample, the only samples possible are those including every kth item from the random starting position.
4) In a cluster sample, the clusters to be included are selected at random and then all members of each selected cluster are included.
5) In a cluster sample, random samples from each strata are included.
6) In a cluster sample, every sample of size n has an equal chance of being included.
7) In a stratified sample, the clusters to be included are selected at random and then all members of each selected cluster are included.
8) In a stratified sample, the only samples possible are those including every kth item from the random starting position.
♦ Relevant knowledge
In statistical analysis an individual sample of an entire population is taken in order to collect details about the population that is of interest. In any method of sampling, the samples must represent the entire population. There are three common sampling techniques that are used: simple random sampling, stratified sampling or cluster sampling.
A stratified and a cluster sample are different because random samples from each stratum are included in a stratified sample.
Cluster samples are made by randomly selecting the clusters that will be included. Then, all members of each cluster are included
Option 2 and Option 4 are correct