What sampling method allows every person in a population to have the same chance of being included in a sample?

The method that allows every person in a population to have the same chance of being included in a sample is known as a Simple Random Sample. This sampling technique ensures that each individual in the population has an equal probability of being selected, which helps eliminate bias in the selection process.

In practical terms, this could be executed using random number generators, drawing names from a hat, or other methods that ensure every individual has an equal chance of inclusion. The principle behind this method is essential for achieving a representative sample that can generalize findings to the broader population effectively.

In contrast, other sampling methods like stratified sampling involve dividing the population into subgroups and then sampling from those subgroups, which can introduce a bias if the divisions do not represent the population correctly. Systematic sampling follows a fixed interval after a random start, which might not guarantee equal chances for everyone in the population. Cluster sampling involves dividing the population into clusters and then randomly selecting entire clusters, which again may not provide an equal opportunity for each individual. Thus, while these methods have their advantages in certain contexts, they do not ensure that every person in the population has the same selection probability as a Simple Random Sample does.