摘要：Benford's Law is used to test for data irregularities. While novel, there are two weaknesses in the current methodology. First, test values used in practice are too conservative and the test values of this paper are more powerful and hold for fairly small samples. Second, testing requires Benford's Law to hold, which it often does not. I present a simple method to transform distributions to satisfy the Law with arbitrary precision and induce scale invariance, freeing tests from the choice of units. I additionally derive a rate of convergence to Benford's Law. Finally, the results are applied to common distributions.
