A statistical population is a set of entities concerning which statistical inferences are to be drawn, often based on a random sample taken from the population. For example, if we were interested in generalizations about crows, then we would describe the set of crows that is of interest. Notice that if we choose a population like all crows, we will be limited to observing crows that exist now or will exist in the future. Probably, geography will also constitute a limitation in that our resources for studying crows are also limited.
Population is also used to refer to a set of potential measurements or values, including not only cases actually observed but those that are potentially observable. Suppose, for example, we are interested in the set of all adult crows now alive in the county of Cambridgeshire, and we want to know the mean weight of these birds. For each bird in the population of crows there is a weight, and the set of these weights is called the population of weights.
A subset of a population is called a subpopulation. If different subpopulations have different properties, they can often be better understood if they are first separated into distinct subpopulations.
For instance, a particular medicine may have different effects on different subpopulations, and its effects may be obscured or dismissed if the subpopulation is not identified and examined in isolation.
Similarly, one can often estimate parameters more accurately if one separates out subpopulations: distribution of heights among people is better modeled by considering men and women as separate subpopulations, for instance.
Populations consisting of subpopulations can be modeled by mixture models, which combine the distributions within subpopulations into an overall population distribution.
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