Which statement best differentiates probability from non-probability sampling?

The statement that probability sampling involves random selection while non-probability sampling does not is the most accurate differentiation between the two sampling methods. In probability sampling, every member of the population has a known and non-zero chance of being selected, which allows for the application of statistical theories and techniques to infer conclusions about the entire population from the sample. This random selection helps eliminate bias and increases the reliability and validity of the results.

In contrast, non-probability sampling lacks this element of randomization. Samples are selected based on subjective criteria or convenience, which may introduce bias and limit the generalizability of the findings. Because not every member of the population has a chance of being included in a non-probability sample, it does not provide the same level of statistical rigor.

The other options do not accurately describe the fundamental difference. Non-probability sampling is not statistically more valid than probability sampling, as it can lead to biased results. Additionally, while it may be true that non-probability sampling can sometimes be quicker to administer, speed is not a defining characteristic that differentiates these methodologies. Finally, the claim that probability sampling excludes certain groups intentionally is misleading; rather, probability sampling aims to include a representative sample of the entire population.