Which sampling method ensures every individual has an equal chance of being included, with independent selections?

The choice of simple random sampling is indeed the correct one for the question regarding a sampling method that ensures every individual has an equal chance of being included, with independent selections.

In simple random sampling, each member of the population has the same probability of being selected, which eliminates bias in the selection process. This method typically involves using random number generators or drawing names from a hat, ensuring that the selections are entirely independent of one another. This independence is crucial because it means the selection of one individual does not affect the chances of another individual being chosen, maintaining the integrity of the sampling process.

In contrast, other sampling methods, such as systematic sampling, stratified sampling, and cluster sampling, do not provide this same level of randomization and equal likelihood of selection across the entire population. For example, systematic sampling selects individuals at regular intervals, which could inadvertently introduce a pattern based on the ordering of the population. Stratified sampling focuses on specific subgroups within the population, selecting individuals from each stratum but not allowing for truly random selection from the entire population. Cluster sampling divides the population into groups and randomly selects whole clusters, which may not represent the entire population adequately.

Thus, simple random sampling is the most effective method for ensuring equal chances and