In probability theory and statistics, the F-distribution, also known as Snedecor's F distribution or the Fisher–Snedecor distribution (after Ronald Fisher and George W. Snedecor) is a continuous probability distribution that arises frequently as the null distribution of a test statistic, most notably in the analysis of variance (ANOVA), e.g., F-test.
Probability density function
Cumulative distribution function
|Parameters||d1, d2 > 0 deg. of freedom|
|Support||x ∈ [0, +∞)|
for d2 > 2
for d1 > 2
for d2 > 4
for d2 > 6
|Ex. kurtosis||see text|
|MGF||does not exist, raw moments defined in text and in |
where I is the regularized incomplete beta function.
The expectation, variance, and other details about the F(d1, d2) are given in the sidebox; for d2 > 8, the excess kurtosis is
The k-th moment of an F(d1, d2) distribution exists and is finite only when 2k < d2 and it is equal to 
The F-distribution is a particular parametrization of the beta prime distribution, which is also called the beta distribution of the second kind.
where U(a, b, z) is the confluent hypergeometric function of the second kind.
- U1 and U2 have chi-squared distributions with d1 and d2 degrees of freedom respectively, and
- U1 and U2 are independent.
Equivalently, the random variable of the F-distribution may also be written
where s12 and s22 are the sums of squares S12 and S22 from two normal processes with variances σ12 and σ22 divided by the corresponding number of χ2 degrees of freedom, d1 and d2 respectively : and .[discuss]
In a frequentist context, a scaled F-distribution therefore gives the probability p(s12/s22 | σ12, σ22), with the F-distribution itself, without any scaling, applying where σ12 is being taken equal to σ22. This is the context in which the F-distribution most generally appears in F-tests: where the null hypothesis is that two independent normal variances are equal, and the observed sums of some appropriately selected squares are then examined to see whether their ratio is significantly incompatible with this null hypothesis.
The quantity X has the same distribution in Bayesian statistics, if an uninformative rescaling-invariant Jeffreys prior is taken for the prior probabilities of σ12 and σ22. In this context, a scaled F-distribution thus gives the posterior probability p(σ22/σ12|s12, s22), where now the observed sums s12 and s22 are what are taken as known.
- If and are independent, then
- If are independent, then
- If (Beta distribution) then
- Equivalently, if X ~ F(d1, d2), then .
- If X ~ F(d1, d2), then has a beta prime distribution: .
- If X ~ F(d1, d2) then has the chi-squared distribution
- F(d1, d2) is equivalent to the scaled Hotelling's T-squared distribution .
- If X ~ F(d1, d2) then X−1 ~ F(d2, d1).
- If X ~ t(n) -- Student's t-distribution—then:
- F-distribution is a special case of type 6 Pearson distribution
- If X and Y are independent, with X, Y ~ Laplace(μ, b) then
- If X ~ F(n, m) then (Fisher's z-distribution)
- The noncentral F-distribution simplifies to the F-distribution if λ = 0.
- The doubly noncentral F-distribution simplifies to the F-distribution if
- If is the quantile p for X ~ F(d1, d2) and is the quantile 1−p for Y ~ F(d2, d1), then
- Lazo, A.V.; Rathie, P. (1978). "On the entropy of continuous probability distributions". IEEE Transactions on Information Theory. IEEE. 24 (1): 120–122.
- Johnson, Norman Lloyd; Samuel Kotz; N. Balakrishnan (1995). Continuous Univariate Distributions, Volume 2 (Second Edition, Section 27). Wiley. ISBN 0-471-58494-0.
- Abramowitz, Milton; Stegun, Irene Ann, eds. (1983) [June 1964]. "Chapter 26". Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Applied Mathematics Series. 55 (Ninth reprint with additional corrections of tenth original printing with corrections (December 1972); first ed.). Washington D.C.; New York: United States Department of Commerce, National Bureau of Standards; Dover Publications. p. 946. ISBN 978-0-486-61272-0. LCCN 64-60036. MR 0167642. LCCN 65-12253.
- NIST (2006). Engineering Statistics Handbook – F Distribution
- Mood, Alexander; Franklin A. Graybill; Duane C. Boes (1974). Introduction to the Theory of Statistics (Third Edition, pp. 246–249). McGraw-Hill. ISBN 0-07-042864-6.
- Taboga, Marco. "The F distribution".
- Phillips, P. C. B. (1982) "The true characteristic function of the F distribution," Biometrika, 69: 261–264 JSTOR 2335882
- M.H. DeGroot (1986), Probability and Statistics (2nd Ed), Addison-Wesley. ISBN 0-201-11366-X, p. 500
- G.E.P. Box and G.C. Tiao (1973), Bayesian Inference in Statistical Analysis, Addison-Wesley. p.110