Which sampling method ensures that every person in the population has an equal chance of selection?

The simple random sample is a sampling method that guarantees every individual in the entire population has an equal chance of being selected. This approach eliminates bias in the selection process and is typically achieved through methods such as random number generation or drawing lots. Because each member of the population can be randomly chosen, it allows for a sample that represents the whole, thus enhancing the validity of the results obtained from the research.

In contrast, the other sampling methods serve different purposes or principles. Stratified sampling divides the population into subgroups or strata based on specific characteristics (like age, gender, or income) and then random samples are taken from each stratum, which may not give all individuals an equal chance depending on the proportions of strata. Cluster sampling involves dividing the population into clusters and then randomly selecting entire clusters, which can lead to unequal representation if certain clusters are more homogeneous than others. Systematic sampling selects members from a population at regular intervals, which may unintentionally introduce bias if there is an underlying pattern in the population.

Overall, the simple random sample is fundamental in research when the goal is to ensure that every individual has an identical opportunity for selection, making it a foundational method in statistical sampling.