In numerical analysis, truncation error is the error made by truncating an infinite sum and approximating it by a finite sum which is present even with infinite-precision arithmetic often caused by truncation of the infinite Taylor seriesto form the algorithm [1]. Truncation error also includes discretization error, which is the error that arises from taking a finite number of steps in a computation to approximate an infinite process. For example, in some numerical methods for ordinary differential equations, the continuously varying function that is the solution of the differential equation is approximated by a process that progresses step by step, and the error that it entails a discretization or truncation error [9]. Occasionally, round-off errorwhich is the consequence of using finite precision floating point numberson computers is also called truncation error, especially if the number is rounded by truncation. In this paper, emphasis is laid on the discretization error.