The error function is a special function defined by integrating the Gaussian:
The leading factor is a normalizing constant chosen so that the value approaches exactly as .
Domain and range It is defined for all real . Because the integrand is always positive, the function increases monotonically, and its range is the open interval .
Symmetry Since the integrand is even, the integral is odd: . The graph is therefore point-symmetric about the origin, through which it passes at .
Monotonicity and slope By the fundamental theorem of calculus, , which is always positive, so the function is strictly increasing. Its slope is steepest at the origin, where it equals .
Asymptotes and limits As , , and as , , giving two horizontal asymptotes . Convergence is rapid: and .
Inflection point The second derivative changes sign at , the unique inflection point, where the curve turns from concave up to concave down.
Relation to other functions It is tied to the standard normal cumulative distribution by . The quantity is the complementary error function .
Applications and history As its name suggests, it originated in the theory of observational errors. Today it appears wherever the Gaussian integral arises: probability calculations for the normal distribution, solutions of the heat and diffusion equations, and bit-error-rate analysis in communications. Because it cannot be expressed with elementary functions, it is treated as a special function in its own right.