What is the key distinction between a sample and a population in statistics?

The distinction between a sample and a population in statistics is pivotal for understanding how data is gathered and analyzed. A population encompasses all members of a specified group that a researcher is interested in studying. This includes every individual or unit within the defined boundaries of the population. For instance, if researchers are studying the height of all adults in a city, the population consists of every adult living in that city.

By contrast, a sample is a smaller subset taken from this population, which is used to draw conclusions about the larger group without needing to study every individual. This allows for more manageable data collection and analysis while still aiming to obtain results that are representative of the entire population. Understanding this key distinction helps clarify how statistical inferences are made based on the data collected from selected samples that are intended to reflect the characteristics of the overall population.