of the random variable will differ from Benford's law by less than The value of depends on the value of and the distribution of the random variable. A method of accounting fraud detection based on bootstrapping and regression has been proposed. If the goal is to conclude agreement with the Benford's law rather than disagreement, then the goodness-of-fit tests mentioned above are inappropriate. In this case the specific tests for equivalence should be applied. An empirical distribution is called equivalent to the Benford's law if a distance (for example total variation distance or the usual Euclidean distance) between the probability mass functions is sufficiently small. This method of testing with application to Benford's law is described in Ostrovski. Range of applicability Distributions known to obey Benford's law Some well-known infinite integer sequences satisfy Benford's law exactly (in the asymptotic limit as more and more terms of the sequence are included). Among these are the Fibonacci numbers, the factorials, the powers of 2, and the powers of almost any other number. Likewise, some continuous processes satisfy Benford's law exactly (in the asymptotic limit as the process continues through time). One is an exponential growth or decay process: If a quantity is exponentially increasing or decreasing in time, then the percentage of time that it has each first digit satisfies Benford's law asymptotically (i.e. increasing accuracy as the process continues through time). Distributions known to disobey Benford's law The square roots and reciprocals of successive natural numbers do not obey this law. Prime numbers in a finite range follow a Generalized Benford’s law, that approaches uniformity as the size of the range approaches infinity. Lists of local telephone numbers violate Benford's law. Benford's law is violated by the populations of all places with a population of at least 2500 individuals from five US states according to the 1960 and 1970 censuses, where only 19 % began with digit 1 but 20 % began with digit 2, because truncation at 2500 introduces statistical bias. The terminal digits in pathology reports violate Benford's law due to rounding. Distributions that do not span several orders of magnitude will not follow Benford's law. Examples include height, weight, and IQ scores. Criteria for distributions expected and not expected to obey Benford's law A number of criteria, applicable particularly to accounting data, have been suggested where Benford's law can be expected to apply. Distributions that can be expected