cities) are likely to satisfy Benford's law very accurately. On the other hand, a distribution mostly or entirely within one order of magnitude (e.g., IQ scores or heights of human adults) is unlikely to satisfy Benford's law very accurately, if at all. However, the difference between applicable and inapplicable regimes is not a sharp cut-off: as the distribution gets narrower, the deviations from Benford's law increase gradually. (This discussion is not a full explanation of Benford's law, because it has not explained why data sets are so often encountered that, when plotted as a probability distribution of the logarithm of the variable, are relatively uniform over several orders of magnitude.) Krieger–Kafri entropy explanation In 1970 Wolfgang Krieger proved what is now called the Krieger generator theorem. The Krieger generator theorem might be viewed as a justification for the assumption in the Kafri ball-and-box model that, in a given base with a fixed number of digits 0, 1, ..., n, ..., , digit n is equivalent to a Kafri box containing n non-interacting balls. A number of other scientists and statisticians have suggested entropy-related explanations for Benford's law. Multiplicative fluctuations Many real-world examples of Benford's law arise from multiplicative fluctuations. For example, if a stock price starts at $100, and then each day it gets multiplied by a randomly chosen factor between 0.99 and 1.01, then over an extended period the probability distribution of its price satisfies Benford's law with higher and higher accuracy. The reason is that the logarithm of the stock price is undergoing a random walk, so over time its probability distribution will get more and more broad and smooth (see above). (More technically, the central limit theorem says that multiplying more and more random variables will create a log-normal distribution with larger and larger variance, so eventually it covers many orders of magnitude almost uniformly.) To be sure of approximate agreement with Benford's law, the distribution has to be approximately invariant when scaled up by any factor up to 10; a log-normally distributed data set with wide dispersion would have this approximate property. Unlike multiplicative fluctuations, additive fluctuations do not lead to Benford's law: They lead instead to normal probability distributions (again by the central limit theorem), which do not satisfy Benford's law. By contrast, that hypothetical stock price described above can be written as the product of many random variables (i.e. the price change factor for