It has struck me often that the target of statistical analysis writ large is randomness. Because it is the fundamental concern of all the techniques for decision making under uncertainty that I try to track–risk analysis, genetic algorithm optimization, and neural network prediction–I assumed I understood the term randomness pretty well.
But it turns out that what you think randomness is depends upon what line of work you’re in. If you are a statistician, randomness occurs in a repeating process the results of which follow no discernible pattern. If you are a geneticist, randomness applies to genetic mutations that are not controlled by genes. If you are a financier, randomness is blamed for controlling stock prices, which respond instantaneously changes changes in available information.
In all these fields, randomness is closely allied with chance and probability, and the human struggle against chance is epic. We resort to sharper and sharper tools to pare down what appears to be random, and just when we think we have the magic tool–say, Monte Carlo software–or the magic idea–the random walk hypothesis, for example–the definition of randomness changes.