Which sampling method ensures every member of the population has an equal chance of being selected?

The correct choice is simple random sampling, which guarantees that every member of the population has an equal likelihood of being selected. In this method, each individual is chosen purely by chance, with no biases or predetermined criteria influencing the selection. This equality of selection is crucial in providing a representative sample that accurately reflects the overall population characteristics, thus enhancing the reliability of statistical inferences made from the sample.

In simple random sampling, the entire population is typically numbered, and selections can be made through random number generators or drawing lots. This ensures that the sampling does not favor any particular segment of the population, eliminating potential bias.

The other sampling methods have different structures and characteristics. Cluster sampling involves dividing the population into clusters and then randomly selecting entire clusters, which may not give each individual in the population an equal chance if the clusters vary significantly. Systematic sampling selects members based on a fixed interval from a randomly chosen starting point, which can lead to unintentional bias if there is a pattern in the population. Probability sampling is a broader category that includes various techniques, not all of which ensure an equal chance for each member. While it is a necessary component of robust statistical methods, it does not specifically refer to the selection mechanism present in simple random sampling.