Frequency probability is the
interpretation of probability that defines an event's
probability as the
limit of its relative frequency in a large number of trials. The frequentist account overcomes some of the problems of the previously dominant viewpoint, the
classical interpretation. Frequentist statistics is often associated with the names of
Jerzy Neyman and
Egon Pearson who described the logic of
statistical hypothesis testing. Other influential figures of the frequentist school include
John Venn and
Richard von Mises.
Frequentists talk about probabilities only when dealing with well-defined random experiments.[citation needed] The set of all possible outcomes of a random experiment is called the sample space of the experiment. An event is defined as a particular subset of the sample space that you want to consider. For any event only one of two possibilities can happen; it occurs or it does not occur. The relative frequency of occurrence of an event, in a number of repetitions of the experiment, is a measure of the probability of that event.
Thus, if nt is the total number of trials and nx is the number of trials where the event x occurred, the probability P(x) of the event occurring will be approximated by the relative frequency as follows
A further and more controversial claim is that in the "long run," as the number of trials approaches infinity, the relative frequency will converge exactly to the probability[1]
