Cluster sampling and stratified sampling both involve selecting subjects in subgroups of the population. what is the difference between those two types of sampling?

In clustering sampling, the researcher test whole groups of people. In stratified, it's systematic choosing.

A census could be: which literary character is more popular in America: Harry potter or Sam Gamgee (Lord of the Rings) ?

First you would choose states to do the testing in, then cities. some of the clusters of people you choose will be tested as a whole (cluster testing). then, you would take random people from the other groups which were not tested as a whole to answer the question. (stratified sampling)

For both methods, divide a population into groups.

For cluster sampling, select all of the members in some of the groups.

For stratified sampling, select a random sample from each group.

Cluster sampling and stratified sampling both involve selecting subjects in subgroups of the population. what is the difference between those two types of sampling?

Answer: Cluster sampling and stratified sampling both involve selecting subjects in subgroups of the population. But the difference is that in cluster sampling all the subjects of the selected subgroup are studied. While in stratified sampling, only randomly selected subjects of subgroups are studied.

Cluster Sampling is a probability sampling method where the target population is divided into clusters. Some of these clusters are selected randomly for sampling and all the members are studied under each randomly selected cluster.

Stratified Sampling is a probability sampling method, in which a population is divided into unique, homogeneous strata, members from these strata are randomly selected to form a sample.