ABSTRACT
The central limit conjecture states that most errors are the result of many small errors and have a normal distribution. The assumption of a normal distribution for error has many advantages and has often been made in applications of statistical models. In the 1930s, the conditions necessary for the central limit conjecture to hold were established and further refined in the 1940s. Simon-Pierre, Marquis de Laplace, set himself the task of using Sir Isaac Newton's laws of motion to derive the exact orbits of the known planets. For his magnificent tour de force, named Mecanique Celeste, he collected all the observations he could find of relative positions in the sky among the planets and the sun. Through the rest of the nineteenth century, Laplace's error function was used by others who wanted to introduce error probabilities into their calculations. What Laplace called the error function has been called the Gaussian distribution, the normal distribution, and the “bell-shaped curve.”
