Get Step-by-step Instructions For Using Benford's Law.doc 2020-2025

For Benford's Law hypothesis testing, commonly employed tests are Pearson's Chi Square test-of-fit, and the Cho-Gaines d statistic.

To apply Benford's Law, therefore, an accountant must count the number of times a 1 appears as the lead digit in the data values, the number of times a 2 appears, etc., and then examine the resulting frequency distribution. The distribution is “natural” if it follows Benford's distribution, and suspect otherwise.

To apply Benford's Law, therefore, an accountant must count the number of times a 1 appears as the lead digit in the data values, the number of times a 2 appears, etc., and then examine the resulting frequency distribution. The distribution is “natural” if it follows Benford's distribution, and suspect otherwise.

Briefly explained, Benford's Law maintains that the numeral 1 will be the leading digit in a genuine data set of numbers 30.1% of the time; the numeral 2 will be the leading digit 17.6% of the time; and each subsequent numeral, 3 through 9, will be the leading digit with decreasing frequency.

The second-order test looks at relationships and patterns in data and is based on the digits of the differences between amounts that have been sorted from smallest to largest (ordered). The digit patterns of the differences are expected to closely approximate the digit frequencies of Benford's Law.

For example, phone numbers in a given area would not follow it because the area code is the same number. Another example would be the recorded times for the Olympic 1,500 m foot race, because none of these times would start with a 1.

This law can be utilized to detect patterns, or lack thereof, in naturally occurring data sets, which can be used to help catch anomalies or fraud in data. It is called Benford's Law. In this article, we will discuss what it is, and why it's important for data science.

What is Benford's Law? Benford's law describes the relative frequency distribution for leading digits of numbers in datasets. Leading digits with smaller values occur more frequently than larger values. This law states that approximately 30% of numbers start with a 1 while less than 5% start with a 9.

Benford's law can uncover fictitious numbers in random data sets because it detects manual intervention in otherwise automated transaction activity. Data manipulated for tax evasion purposes will likely deviate from Benford's law. A Benford's law analysis presents a null hypothesis and an alternate hypotheses.