Which sampling method gives every member of the population an equal chance of being chosen?

• A. Stratified random sampling
• B. Simple random sampling
• C. Systematic random sampling
• D. Cluster sampling

Answer: (B)

Having equal likelihood for every member means each person in the population has the same chance of being included in the sample. This is what simple random sampling does: you treat every individual as equally eligible, and you pick the required number of people completely at random, often by assigning numbers to everyone and using a random generator or drawing names from a hat. In this setup, each person has probability m/N of being chosen (where m is the sample size and N is the population size), so the selection is purely by chance with no bias from groupings or order.

Stratified sampling, by contrast, divides the population into groups and samples from each group to ensure representation across those groups, which means some individuals’ chances depend on which group they belong to. Systematic sampling chooses every k-th person after a random start, which can still give equal inclusion probability in principle, but it relies on the list order and can be biased if there’s any pattern in the ordering. Cluster sampling picks whole clusters and then surveys within those clusters, so an individual’s chance of being included depends on the cluster they belong to.