Population and sample
The Difference between Population and Sample is given here. In statistics, the population and the sample are two completely different concepts used in the field and individual studies.
The statistical population is also called the universe. It is a set of elements on which studies and observations are made.
The population is a random variable or magnitude of a random nature. When defining the variables of a study, the population to be investigated must be defined.
The population is the set made up of all the elements to be studied.
In some cases the populations are too extensive to be studied, in that case, it is necessary to select a sample to which the studies will be applied in order to evaluate characteristics and phenomena present in the population.
In statistics, a sample is a subset of cases or individuals in a population. The sample must be representative so the sampling (selection) technique must be adequate.
When selecting a sample, one should avoid choosing a biased sample whose usefulness will be limited because it will not adequately represent the phenomenon or characteristic to be studied.
The objective of the samples is to infer the properties of the entire population.
Sampling is much more accurate than studying an entire population since handling a smaller number of data causes fewer errors in handling.
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Difference between Population and Sample
- A sample is a subset of individuals, events, or objects that are selected from a larger population.
- The population is a larger group that is not selected randomly, since objects, events, and subjects that present a specific phenomenon are selected.
- Sampling is the collection of a sample directly from the population to be studied. Sampling must be random and allows a better study of the phenomenon or event.