Statistical Population

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A Statistical Population is an entire set of items, events, subjects or measurements from which a sample can be drawn for a statistical experiment or random trial.



  • (Wikipedia, 2015) ⇒ Retrieved:2015-2-23.
    • In statistics, a population is a set of similar items or events which is of interest for some question or experiment.[1] A statistical population can be a group of actually existing objects (e.g. the set of all stars within the Milky Way galaxy) or a hypothetical and potentially infinite group of objects conceived as a generalization from experience (e.g. the set of all possible hands in a game of poker).[2] A common aim of statistical analysis is to produce information about some chosen population.[3]
In statistical inference, a subset of the population (a statistical sample) is chosen to represent the population in a statistical analysis.[4] If a sample is chosen properly, characteristics of the entire population that the sample is drawn from can be estimated from corresponding characteristics of the sample.

Depending on the sampling method, a sample can have fewer observations than the population, the same number of observations, or more observations. More than one sample can be derived from the same population.
Other differences have to do with nomenclature, notation, and computations. For example, a measurable characteristic of a population, such as a mean or standard deviation, is called a parameter; but a measurable characteristic of a sample is called a statistic.

To understand the basic foundation for hypothesis testing and other types of inferential statistics, it’s important to understand how a sample and a population differ.
A population is a collection of people, items, or events about which you want to make inferences. It is not always convenient or possible to examine every member of an entire population. For example, it is not practical to count the bruises on all apples picked at an orchard. It is possible, however, to count the bruises on a set of apples taken from that population. This subset of the population is called a sample.
If the sample is random and large enough, you can use the information collected from the sample to make inferences about the population. For example, you could count the number of apples with bruises in a random sample and then use a hypothesis test to estimate the percentage of all the apples that have bruises.