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A plot of the Q-function.

In statistics, the Q-function is the tail probability of the standard normal distribution \phi(x).[1][2] In other words, Q(x) is the probability that a normal (Gaussian) random variable will obtain a value larger than x standard deviations above the mean.

If the underlying random variable is y, then the proper argument to the tail probability is derived as:

x=\frac{y - \mu}{\sigma}

which expresses the number of standard deviations away from the mean.

Other definitions of the Q-function, all of which are simple transformations of the normal cumulative distribution function, are also used occasionally.[3]

Because of its relation to the cumulative distribution function of the normal distribution, the Q-function can also be expressed in terms of the error function, which is an important function in applied mathematics and physics.

Definition and basic properties[edit]

Formally, the Q-function is defined as

Q(x) = \frac{1}{\sqrt{2\pi}} \int_x^\infty \exp\left(-\frac{u^2}{2}\right) \, du.


Q(x) = 1 - Q(-x) = 1 - \Phi(x)\,\!,

where \Phi(x) is the cumulative distribution function of the normal Gaussian distribution.

The Q-function can be expressed in terms of the error function, or the complementary error function, as[2]

Q(x) &=\frac{1}{2}\left( \frac{2}{\sqrt{\pi}} \int_{x/\sqrt{2}}^\infty \exp\left(-t^2\right) \, dt \right)\\
&= \frac{1}{2} - \frac{1}{2} \operatorname{erf} \left( \frac{x}{\sqrt{2}} \right) ~~\text{ -or-}\\
&= \frac{1}{2}\operatorname{erfc} \left(\frac{x}{\sqrt{2}} \right).

An alternative form of the Q-function known as Craig's formula, after its discoverer, is expressed as:[4]

Q(x) = \frac{1}{\pi} \int_0^{\frac{\pi}{2}} \exp \left( - \frac{x^2}{2 \sin^2 \theta} \right) d\theta.

This expression is valid only for positive values of x, but it can be used in conjunction with Q(x) = 1 − Q(−x) to obtain Q(x) for negative values. This form is advantageous in that the range of integration is fixed and finite.

\left (\frac{x}{1+x^2} \right ) \phi(x) < Q(x) < \frac{\phi(x)}{x}, \qquad x>0,
become increasingly tight for large x, and are often useful.
Using the substitution v =u2/2, the upper bound is derived as follows:
Q(x) =\int_x^\infty\phi(u)\,du <\int_x^\infty\frac ux\phi(u)\,du =\int_{\frac{x^2}{2}}^\infty\frac{e^{-v}}{x\sqrt{2\pi}}\,dv=-\biggl.\frac{e^{-v}}{x\sqrt{2\pi}}\biggr|_{\frac{x^2}{2}}^\infty=\frac{\phi(x)}{x}.
Similarly, using \phi'(u) = - u \phi(u) and the quotient rule,
\left(1+\frac1{x^2}\right)Q(x) =\int_x^\infty \left(1+\frac1{x^2}\right)\phi(u)\,du >\int_x^\infty \left(1+\frac1{u^2}\right)\phi(u)\,du =-\biggl.\frac{\phi(u)}u\biggr|_x^\infty
Solving for Q(x) provides the lower bound.
Q(x)\leq e^{-\frac{x^2}{2}}, \qquad x>0
  • Improved exponential bounds and a pure exponential approximation are [5]
Q(x)\leq \tfrac{1}{4}e^{-x^2}+\tfrac{1}{4}e^{-\frac{x^2}{2}} \leq \tfrac{1}{2}e^{-\frac{x^2}{2}}, \qquad x>0
Q(x)\approx \frac{1}{12}e^{-\frac{x^2}{2}}+\frac{1}{4}e^{-\frac{2}{3} x^2}, \qquad x>0
  • A tight approximation of Q(x) for x \in [0,\infty) is given by Karagiannidis & Lioumpas (2007)[6] Fixed who showed for the appropriate choice of parameters \{A, B\} that
f(x; A, B) = \frac{\left(1 - e^{-Ax}\right)e^{-x^2}}{B\sqrt{\pi} x} \approx \operatorname{erfc} \left(x\right).
The absolute error between f(x; A, B) and \operatorname{erfc}(x) over the range [0, R] is minimized by evaluating
\{A, B\} = \underset{\{A,B\}}{arg\ min} \frac{1}{R} \int_0^R | f(x; A, B) - \operatorname{erfc}(x) |dx.
Using R = 20 and numerically integrating, they found the minimum error occurred when \{A, B\} = \{1.98, 1.135\}, which gave a good approximation for \forall x \ge 0.
Substituting these values and using the relationship between Q(x) and \operatorname{erfc}(x) from above gives
 Q(x)\approx\frac{\left(  1-e^{-1.4x}\right)  e^{-\frac{x^{2}}{2}}}{1.135\sqrt{2\pi}x}, x \ge 0.

Inverse Q

The inverse Q-function can be trivially related to the inverse error function:

Q^{-1}(x) = \sqrt{2}\ \mathrm{erf}^{-1}(1-2x)


The Q-function is well tabulated and can be computed directly in most of the mathematical software packages such as Matlab and Mathematica. Some values of the Q-function are given below for reference.


  1. ^ The Q-function, from cnx.org
  2. ^ a b Basic properties of the Q-function
  3. ^ Normal Distribution Function - from Wolfram MathWorld
  4. ^ John W. Craig, A new, simple and exact result for calculating the probability of error for two-dimensional signal constellaions, Proc. 1991 IEEE Military Commun. Conf., vol. 2, pp. 571–575.
  5. ^ Chiani, M., Dardari, D., Simon, M.K. (2003). New Exponential Bounds and Approximations for the Computation of Error Probability in Fading Channels. IEEE Transactions on Wireless Communications, 4(2), 840–845, doi=10.1109/TWC.2003.814350.
  6. ^ Karagiannidis, G. K., & Lioumpas, A. S. (2007). An improved approximation for the Gaussian Q-function. Communications Letters, IEEE, 11(8), 644-646.