Statisticians define a population as the entire collection of items that is the focus of concern. The branch of Statistics called Descriptive Statistics, provides us with ways to describe the characteristics of a given population by measuring each of its items and then summarizing the set of measures in various ways.

The branch of Statistics called Inferential Statistics consists of procedures to make educated inferences about the characteristics of a population by drawing a random sample and appropriately analyzing the information it provides.

A population can be of any size and while the items need not be uniform, the items must share at least one measurable feature. For example here is a population of 9 persons. While no two of the persons are identical they have many features in common. Each of the persons in this population has a weight, a height, a hat size and a shoe size, among many other potential features. The set of 9 measurements of any one of these features would, in statistical terms, be defined as a population.

The critical difference between a population and a sample, is that with a population our interest is to identify its characteristics whereas with a sample, our interest is to make inferences about the characteristics of the population from which the sample was drawn.

It is noteworthy that while the illustrated population is fairly small, it contains only 9 items, a different population might be extremely large, It might, for example, consist of all of the persons in a given city, country, planet, or even universe.