What defines probability sampling methods?

Probability sampling methods are defined by their systematic approach to selecting participants from a population, which is based on random selection. This randomness ensures that every individual has a known, non-zero chance of being included in the sample. The correct choice emphasizes that these methods allow for a predictable selection likelihood. This means researchers can calculate the probability of each member of the population being selected, facilitating more robust statistical analysis and generalizations to the larger population.

In contrast, the other options miss the essential element of probability sampling. For instance, the idea of certainty in selection likelihood is not accurate, as while these methods allow for probability estimations, they do not guarantee that any specific individual will be selected. Furthermore, randomness is a foundational characteristic of these methods, directly contradicting the notion that they do not allow for randomness. Relying solely on expert opinion indicates a non-probability sampling method, which can introduce bias and does not provide a systematic way to represent the population accurately.

Overall, the focus on probability in these sampling methods emphasizes their strength in ensuring the generalizability of findings through the randomness and known selection likelihood they provide.