of will approach as Therefore, the sample average is such that converges to the normal distribution from which the central limit theorem follows. Convergence to the limit The central limit theorem gives only an asymptotic distribution. As an approximation for a finite number of observations, it provides a reasonable approximation only when close to the peak of the normal distribution; it requires a very large number of observations to stretch into the tails. The convergence in the central limit theorem is uniform because the limiting cumulative distribution function is continuous. If the third central moment exists and is finite, then the speed of convergence is at least on the order of (see Berry–Esseen theorem). Stein's method can be used not only to prove the central limit theorem, but also to provide bounds on the rates of convergence for selected metrics. The convergence to the normal distribution is monotonic, in the sense that the entropy of increases monotonically to that of the normal distribution. The central limit theorem applies in particular to sums of independent and identically distributed discrete random variables. A sum of discrete random variables is still a discrete random variable, so that we are confronted with a sequence of discrete random variables whose cumulative probability distribution function converges towards a cumulative probability distribution function corresponding to a continuous variable (namely that of the normal distribution). This means that if we build a histogram of the realizations of the sum of independent identical discrete variables, the curve that joins the centers of the upper faces of the rectangles forming the histogram converges toward a Gaussian curve as approaches infinity, this relation is known as de Moivre–Laplace theorem. The binomial distribution article details such an application of the central limit theorem in the simple case of a discrete variable taking only two possible values. Relation to the law of large numbers The law of large numbers as well as the central limit theorem are partial solutions to a general problem: "What is the limiting behavior of as approaches infinity?" In mathematical analysis, asymptotic series are one of the most popular tools employed to approach such questions. Suppose we have an asymptotic expansion of : Dividing both parts by and taking the limit will produce , the coefficient of the highest-order term in the expansion, which represents the rate at which changes in its leading term. Informally, one can say: "