More on Normal Distributions

First, this section talks about the history of the normal distribution and the central limit theorem and the relation of normal distributions to errors. Then, it discusses how to compute the area under the normal curve. It then moves on to the normal distribution, the area under the standard normal curve, and how to translate from non-standard normal to standard normal. Finally, it addresses how to compute (cumulative) binomial probabilities using normal approximations.


  1. de Moivre reasoned that if he could find a mathematical expression for the smooth curve that came about when the number of binomial events (coin flips) increased, he would be able to solve problems such as finding the probability of 60 or more heads out of 100 coin flips much more easily. The curve he discovered is now called the "normal curve".

  2. For example, one of the first applications of the normal distribution was to the analysis of errors of measurement made in astronomical observations. Galileo in the 17th century noted that these errors were symmetric and that small errors occurred more frequently than large errors.

  3. Laplace derived the central limit theorem. Adrian and Gauss developed the formula for the normal distribution.