What sampling method ensures each member of the population has an equal chance of being included in the sample?

What sampling method ensures each member of the population has an equal chance of being included in the sample?

• A. Systematic Sampling
• B. Convenience Sampling
• C. Random Sampling
• D. Stratified Sampling

Answer: (C)

Equal chance for every member of the population to be included is the hallmark of simple random sampling. In this approach, each person has the same probability of being selected, typically by giving everyone a unique number and using a random mechanism (like a random number generator or a random number table) to pick the sample. This equal-probability method helps ensure that the sample is unbiased and representative of the whole population, especially as the sample size grows. Other methods don’t guarantee that uniform chance across all individuals. Systematic sampling selects every kth member after a random starting point, which can approximate equal chances but may introduce bias if there’s a hidden pattern in the population that aligns with the interval. Convenience sampling chooses whatever is easiest to access, which tends to bias the sample toward certain traits. Stratified sampling divides the population into subgroups and samples within them to improve representation; however, unless the selection within each subgroup is itself random and allocations are carefully planned, not every individual in the total population has an equal chance of being chosen.

Equal chance for every member of the population to be included is the hallmark of simple random sampling. In this approach, each person has the same probability of being selected, typically by giving everyone a unique number and using a random mechanism (like a random number generator or a random number table) to pick the sample. This equal-probability method helps ensure that the sample is unbiased and representative of the whole population, especially as the sample size grows.

Other methods don’t guarantee that uniform chance across all individuals. Systematic sampling selects every kth member after a random starting point, which can approximate equal chances but may introduce bias if there’s a hidden pattern in the population that aligns with the interval. Convenience sampling chooses whatever is easiest to access, which tends to bias the sample toward certain traits. Stratified sampling divides the population into subgroups and samples within them to improve representation; however, unless the selection within each subgroup is itself random and allocations are carefully planned, not every individual in the total population has an equal chance of being chosen.