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In statistics the **frequency** of an event *i* is the number *n _{i}* of times the event occurred in the experiment or the study. These frequencies are often graphically represented in histograms.

We speak of **absolute frequencies**, when the counts *n _{i}* themselves are given and of

**(relative) frequencies**, when those are normalized by the total number of events:

$ f_i = \frac{n_i}{N} = \frac{n_i}{\sum_i n_i} $

Taking the *f _{i}* for all

*i*and tabulating or plotting them leads to a frequency distribution.

The **relative frequency density** of occurrence of an event is the relative frequency of *i* divided by the size of the bin used to classify *i*.

In biology, relative frequency is the occurrence of a single gene in a specific species that makes up a gene pool.

The **limiting relative frequency** of an event over a long series of trials is the conceptual foundation of the frequency interpretation of probability. In this framework, it is assumed that as the length of the series increases without bound, the fraction of the experiments in which we observe the event will stabilize. This interpretation is often contrasted with Bayesian probability.

## See also Edit

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