The theory and practice of finance draws heavily on probability theory. All MBA programs prepare finance majors for their career in the profession by requiring one generalist course in probability theory and statistics attended by all business majors. While several probability distributions are covered in the course, the primary focus is on the normal or Gaussian distribution. Students find it easy to understand and apply the normal distribution: Give them the expected value and standard deviation and probability statements about outcomes can be easily made. Moreover, even if a random variable of interest is not normally distribution, students are told that a theorem in statistics called the Central Limit Theorem proves that under certain conditions the sum of independent random variables will be asymptotically normally distributed. Loosely speaking, this means that as the number of random variables are summed, the sum will approach a normal distribution. Armed with this rudimentary knowledge of probability theory, finance students march into their elective courses in finance that introduce them to the quantitative measures of risk (the standard deviation) and the quantitative inputs needed to implement modern portfolio theory (the expected value or mean and the standard deviation). In listing assumptions for most theories of finance, the first assumption on the list is often: “Assume asset returns are normally distributed.” The problem, however, is that empirical evidence does not support the assumption that many important variables in finance follow a normal distribution. The application of the Central Limit Theorem to such instances is often inappropriate because the conditions necessary for its application are not satisfied. And this brings us to the purpose of this book. Our purpose is fourfold. First, we explain alternative probability distributions to the normal distributions for describing asset returns as well as defaults. We focus on the stable Paretian (or alpha stable) distribution because of the strong support for that distribution that dates back four decades to the seminal work of Benoit Mandelbrot. Second, we explain how to estimate distributions. Third, we present empirical evidence rejecting the hypothesis that returns for stocks and bonds are normally distributed T and instead show that they exhibit fat tails and skewness. Finally, we explain the implications of fat tails and skewness to portfolio selection, risk management, and option pricing. We must admit that our intent at the outset was to provide a “nontechnical” treatment of the topic. However, we could not do so. Rather, we believe that we have provided a less technical treatment than is provided in the many excellent books and scholarly articles that deal with probability and statistics applied to finance and risk management. The book is not simple reading. It must be studied to appreciate the pitfalls that result from the application of the normal distribution to real-world financial problems.