Which feature distinguishes probability sampling from nonprobability sampling?

• A. In probability sampling, every member has a known, nonzero chance of selection
• B. Probability sampling yields biased results
• C. Nonprobability sampling is always more representative
• D. Probability sampling requires smaller sample sizes

Answer: (A)

Probability sampling relies on a random mechanism where each member of the population has a known, nonzero chance of being selected. This setup lets researchers quantify sampling error and make generalizations to the population because the selection process is defined and unbiased by handpicking or convenience. The explicit knowledge of selection probabilities is what gives probability sampling its statistical foundations and allows valid inferences from the sample to the population. It’s not correct to say that probability sampling inherently yields bias; the goal of this approach is to minimize bias through random selection. Conversely, nonprobability sampling often lacks known selection probabilities, which can lead to biased samples and limits on how confidently the results generalize. It is also not true that probability sampling always requires smaller samples—the needed size depends on the precision desired and the design, not on the sampling method itself.