The F-distribution can be used for several types of applications, including testing hypotheses about the equality of two population variances and testing the validity of a multiple regression equation. Common examples of discrete distributions are binomial distribution, Poisson distribution, Hyper-geometric distribution and multinomial distribution. Suppose that P is a continuous distribution on S with probability density function f. An element x ∈ S that maximizes f is a mode of the distribution. Continuous distributions can be expressed with a continuous function or graph; In continuous distributions, graph consists of a smooth curve; To calculate the chance of an interval, we required integrals; P(Y = y) = 0 for any distinct value y. P(Y
The F distribution (Snedecor's F distribution or the Fisher Snedecor distribution) represents continuous probability distribution which occurs frequently as null distribution of test statistics. In probability and statistics, the t-distribution is any member of a family of continuous probability distributions that arises when estimating the mean of a normally distributed population in situations where the sample size is small and population standard deviation is unknown. The F-distribution is a continuous probability distribution, which means that it is defined for an infinite number of different values. The particular F-distribution that we use for an application depends upon the number of degrees of freedom that our sample has.

It happens mostly during analysis of variance or F-test.

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