What distinguishes a population from a sample in statistics?

The distinction between a population and a sample in statistics is fundamental to data analysis and research design. A population refers to the complete set of all individuals or items that are of interest to a researcher. This can include everyone in a specific demographic, all responses to a survey, or every item produced in a factory, depending on the context of the study. When you talk about a population, you are considering the entirety of a relevant group.

On the other hand, a sample is a subset of that population. It contains only a portion of the members from the population and is used when it is impractical or impossible to study the entire population due to constraints like time, resources, or accessibility. Researchers often select samples using various sampling methods to draw conclusions about a broader population without needing to include every single member.

The other options present scenarios that do not accurately reflect the definitions and understandings of populations and samples. It is essential to grasp that while a population encompasses all members, a sample is limited to a selection, which is key in the study of statistics.