Which of the following is a probability sampling technique?

Cluster sampling is a probability sampling technique that works by dividing the population into separate groups, or clusters, and then selecting entire clusters randomly for inclusion in the study. This method ensures that every individual within the clusters has an equal chance of being included in the sample, thereby maintaining the principles of randomization that define probability sampling.

In contrast, methods such as purposive sampling, quota sampling, and convenience sampling do not adhere to the principles of probability sampling. Purposive sampling selects participants based on specific characteristics or criteria, which can introduce bias. Quota sampling involves filling a predefined quota of participants with certain traits, often based on convenience rather than random selection. Convenience sampling relies on selecting individuals who are easiest to access, rather than offering every individual an equal opportunity to be chosen, which can lead to a biased representation of the population. Therefore, cluster sampling is distinguished by its use of randomness, ensuring a more representative and unbiased sample of the population from which conclusions can be drawn.