In statistics, the term population is used to describe the subjects of a particular study—everything or everyone who is the subject of a statistical observation. Populations can be large or small in size and defined by any number of characteristics, though these groups typically defined specifically rather than vaguely—for instance, a population of women over 18 who buy coffee at Starbucks rather than a population of women over 18.

Statistical populations are used in order to observe behaviors, trends, and patterns in the way individuals in a defined group interact with the world around them, allowing statisticians to draw conclusions about the characteristics of the subjects of study; although these subjects are most often humans and animals, plants and even stars can be grouped as populations in statistics.

There are, of course, certain limitations with studying populations, mostly in that it is rare to be able to observe all of the individuals in any given group. For this reason, scientists who use statistics also study subpopulations and take statistical samples of small portions of larger populations in order to more accurately analyze the full spectrum of behaviors and characteristics of the population at large.

### Understanding What Constitutes a Population

Basically, a statistical population is any group of individuals that are the subject of a study, meaning that almost anything can make up a population so long as the individuals can be grouped together by a common feature—in a study that is trying to determine the mean weight of all 20-year-old males in the United States, the population is all 20-year-old males in the United States.

Another example would be a study that investigates how many people live in Argentina wherein the population would be every person living in Argentina, regardless of citizenship, age, or gender, though the population in a separate study that asked how many men under 25 lived in Argentina would be all men who were 24 and under who lived in Argentina regardless of citizenship.

Essentially, statistical populations can be as vague or specific as the statistician desires, it ultimately depends on the goal of the research being conducted. A cow farmer wouldn't want to know the stats on how many red female cows he had, he would want to know the stats on how many females he had that were still able to produce calves, so that farmer would want to select the latter as his population of study.

### Subpopulations and Statistical Samples

Although the population is what scientists wish to study in whole, it is very rare to be able to perform a census of every individual member of the population. Due to constraints of resources, time, and accessibility, it is nearly impossible to perform a measurement on every subject, so inferential statistics steps in and scientists are able to study only a small portion of the population and still observe tangible results.

Rather than performing measurements on every member of the population, scientists consider a subset of this population called a statistical sample. These samples provide measurements of the individuals that tell us about corresponding measurements in the population, which can then be repeated and compared with different statistical samples in order to more accurately describe the whole population.

The question of which subset should be selected, then, is highly important in the study of statistics, and there are a variety of different ways to select a sample, many of which will not produce any meaningful results. For this reason, scientists are constantly on the lookout for potential subpopulations because they typically obtain better results when recognizing the mixture of types of individuals in the populations being studied.

Different sampling techniques, such as forming stratified samples, can help in dealing with subpopulations, and many of these techniques assume that a specific type of sample, called a simple random sample, has been selected from the population.