Which sampling method ensures each member of the population has an equal and independent chance of being selected?

Which sampling method ensures each member of the population has an equal and independent chance of being selected?

• A. Simple Random Sampling
• B. Stratified Sampling
• C. Cluster Sampling
• D. Systematic Sampling

Answer: (A)

Equal and independent chances of selection are the hallmark of simple random sampling. In this approach, every member of the population has the same probability of being chosen, and the selection of one person doesn’t bias or affect the chances for others. Typically this is done by assigning each person a unique number and using a random process (like a random number generator or drawing lots) to pick the required number of individuals. Even when you sample without replacement, each member’s initial chance of being included is the same, and the random process treats everyone equally, avoiding systematic favoritism. Other methods change how probabilities are distributed. Stratified sampling divides the population into subgroups and samples within them, so an individual’s chance of selection depends on their subgroup and the allocation used. Cluster sampling groups units into clusters and then samples whole clusters, which means people in nonselected clusters have zero chance of being included. Systematic sampling selects every kth unit after a random start, which can introduce bias if there’s any pattern in the list, and it does not inherently guarantee equal, independent odds for every individual.

Equal and independent chances of selection are the hallmark of simple random sampling. In this approach, every member of the population has the same probability of being chosen, and the selection of one person doesn’t bias or affect the chances for others. Typically this is done by assigning each person a unique number and using a random process (like a random number generator or drawing lots) to pick the required number of individuals. Even when you sample without replacement, each member’s initial chance of being included is the same, and the random process treats everyone equally, avoiding systematic favoritism.

Other methods change how probabilities are distributed. Stratified sampling divides the population into subgroups and samples within them, so an individual’s chance of selection depends on their subgroup and the allocation used. Cluster sampling groups units into clusters and then samples whole clusters, which means people in nonselected clusters have zero chance of being included. Systematic sampling selects every kth unit after a random start, which can introduce bias if there’s any pattern in the list, and it does not inherently guarantee equal, independent odds for every individual.