This very small introduction to the Central Limit Theorem is probably something worthwhile before the Shannon paper. The main point is that as we take more and more samples from a random variable, approved

medicine with a fixed mean and variance, medical the samples approach a normal (Gaussian) distribution. That is, irregardless of the distribution of the random variable, if it meets the criteria, it will behave like a normal ly distributed random variable in the limiting case. The typical application engineering application of this theorem is making the assumption that some measured quantity is normally distributed and use that assumption to define things like confidence limits and so forth. The requirements for this assumption are that the process is second-order stationary, meaning the mean and variance do not change in the window of observation, and that the number of samples is approaching infinity. The requirement for a large number of samples can sometimes be loosened since the residual differences between the sample distribution and a normal distribution can sometimes be determined. The requirement for a stationary process cannot. For example, it would be foolish to apply Gaussian statistics to a random-walk (Brownian motion).

The key message is that the normal/Gaussian assumption is typically a good one, as long as the statistical nature of the random variable under investigation is constant through the period of observation and the number of samples is large.

( sec_4_f.pdf ) ( Image is from Wikipedia )