Answer
In simple random sampling, each sample of the same size has the same probability of being selected.
In systematic random sampling, we first randomly select one member from the first k units of the list of elements arranged based on a given characteristic where k is the number obtained by dividing the population size by the intended sample size. Then every kth member, starting with the first selected member, is included in the sample.
In a stratified random sample, we first divide the population into sub-populations, which are called strata. Then, one sample is selected from each of these strata. The collection of all samples from all strata gives the stratified random sample.
In cluster sampling, the whole population is first divided into clusters. Each cluster is representative of the population. Then a random sample of clusters is selected. Finally, a random sample of elements from each of the selected clusters is selected.
Work Step by Step
In simple random sampling, each sample of the same size has the same probability of being selected.
In systematic random sampling, we first randomly select one member from the first k units of the list of elements arranged based on a given characteristic where k is the number obtained by dividing the population size by the intended sample size. Then every kth member, starting with the first selected member, is included in the sample.
In a stratified random sample, we first divide the population into sub-populations, which are called strata. Then, one sample is selected from each of these strata. The collection of all samples from all strata gives the stratified random sample.
In cluster sampling, the whole population is first divided into clusters. Each cluster is representative of the population. Then a random sample of clusters is selected. Finally, a random sample of elements from each of the selected clusters is selected.
