What distinguishes a population from a sample in statistics?

In statistics, a population is defined as the complete set of items or individuals that share a common characteristic or are being studied. It includes every member within the specified group that is relevant to the research question. This could be all the people, measurements, or observations that fit the criteria set for the study.

On the other hand, a sample is a subset of the population, selected for the purpose of analysis. It is smaller than the population and is utilized to make inferences or predictions about the population as a whole. The use of samples is crucial because studying an entire population can often be impractical, time-consuming, or impossible.

Thus, the distinction lies primarily in the scope: a population encompasses all members of a defined group, while a sample contains only those members selected for the study. This understanding is vital for conducting sound statistical analysis and for drawing valid conclusions from research.