Narrative and Numbers: The Value of Stories in Business (Columbia Business School Publishing)

by Aswath Damodaran

Normality is not the norm: One of the shameful legacies of statistics classes is that the only distribution most of us remember is the normal distribution. It is an extremely elegant and convenient distribution, since it can not only be fully characterized by just two summary statistics, the mean and the standard deviation, but it lends itself to probability statements such as “that has only a 1 percent chance of happening since it is 3 standard deviations away from the mean.” Unfortunately, most real-world phenomena are not normally distributed, and that is especially true for data we look at in business and finance. In spite of that, analysts and researchers continue to use the normal distribution as their basis for making predictions and building models and are constantly surprised by outcomes that fall outside their ranges.1 3. The outlier problem: The problem with outliers is that they make your findings weaker. Not surprisingly, the way researchers respond to outliers is by ridding themselves of the source of the trouble. Removing outliers, though, is a dangerous game; it opens the door to bias, since outliers that don’t fit your priors are quick to be removed, but outliers that do fit your priors are maintained. In fact, if you view your job in business and investing as dealing with crises, you can argue that it is the outliers you should be paying the most attention to, not the data that neatly fits your hypothesis.