# Some New Tests of Conformity with Benford’s Law

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## Abstract

**:**

## 1. Introduction

## 2. New Tests of Conformity with Benford’s Law

**Proposition 1.**

**Proof.**

**Remark**

**1.**

**Remark**

**2.**

**Proposition 2.**

**Proof.**

**Remark 3.**

**Remark 4.**

## 3. Monte Carlo Simulations

- Uniform mixture: ${p}_{A}$ describes the discrete uniform distribution with the same support as the considered Benford’s distribution;
- Normal mixture: ${p}_{Ai}$ are the probabilities of $N({\mu}_{B},{\sigma}^{2})$, with ${\mu}_{B}$ the mean of Benford’s distribution and $\sigma =\sqrt{4{\mu}_{B}}$;
- Randomly perturbed mixture: Benford’s law is perturbed by a random quantity in correspondence to each digit. More precisely, ${p}_{Ai}={u}_{i}{p}_{{B}_{i}}$ with ${u}_{i}\sim U(0,2{p}_{{B}_{i}})$. Since this mixture contains elements of randomness, each Monte Carlo iteration uses a different mixture. However, the mixtures are the same across all tests;
- Under-reporting mixture: under the alternative, Benford’s distribution is modified by putting to zero the probability of “round” numbers and giving this probability to the preceding number: for example, ${p}_{A20}=0$ and ${p}_{A19}={p}_{B19}+{p}_{B20}$. This mixture is only considered with reference to the first two digits case.

#### 3.1. First-Digit Law

#### 3.2. First Two Digits Law

## 4. Statistical versus Practical Significance

“Virtually all specific null hypotheses will be rejected using present standards. It will probably be necessary to replace the concept of statistical significance with some measure of economic significance.”

“What is needed is a test that ignores the number of records. The mean absolute deviation ($MAD$) test is such a test, and the formula is shown in Equation 7.7. [...] There is no reference to the number of records, N, in Equation 7.7.”

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Proof of Remark 4.**

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**Figure 1.**Probability function of the first-two-digits Benford’s law (red) compared to the probability functions of the mixtures used under the alternative hypothesis (blue). In the figure, a mixing parameter $\lambda =0.6$ was used to exaggerate the visual effect. Larger values of $\lambda $ were used in the simulations, with the consequence that the distribution under the alternative is closer to the distribution under the null.

**Figure 2.**First digit tests: deviation of the actual size from the nominal size. Tests are as follows: “Adj. MAD”, adjusted MAD (10); “Chi-sq(2)”, ${\chi}^{2}\left(2\right)$ test on mean and variance (4); “Chi-sq(d-1)”, ${\chi}^{2}\left(8\right)$ test (9); “Mean”, normal test on the mean (1); “Mean & var.”, normal test on mean and variance (3); “Variance”, normal test on variance (2). The number of observations is indicated on top of each panel.

**Figure 3.**First digit tests: size–power curves of the tests against the uniform mixing alternative with $\lambda =0.9$. Tests are as follows: “Adj. MAD”, adjusted MAD (10); “Chi-sq(2)”, ${\chi}^{2}\left(2\right)$ test on the mean and variance (4); “Chi-sq(d-1)”, ${\chi}^{2}\left(8\right)$ test (9); “Mean”, normal test on the mean (1); “Mean & var.”, normal test on mean and variance (3); “Variance”, normal test on variance (2). The dashed line is $power=actual\phantom{\rule{0.277778em}{0ex}}size$. The number of observations is indicated on top of each panel.

**Figure 4.**First digit tests: size–power curves of the tests against the normal mixing alternative with $\lambda =0.9$. Tests are as follows: “Adj. MAD”, adjusted MAD (10); “Chi-sq(2)”, ${\chi}^{2}\left(2\right)$ test on mean and variance (4); “Chi-sq(d-1)”, ${\chi}^{2}\left(8\right)$ test (9); “Mean”, normal test on the mean (1); “Mean & var.”, normal test on mean and variance (3); “Variance”, normal test on variance (2). The dashed line is $power=actual\phantom{\rule{0.277778em}{0ex}}size$. The number of observations is indicated on top of each panel.

**Figure 5.**First digit tests: size–power curves of the tests against the perturbed mixing alternative with $\lambda =0.75$. Tests are as follows: “Adj. MAD”, adjusted MAD (10); “Chi-sq(2)”, ${\chi}^{2}\left(2\right)$ test on mean and variance (4); “Chi-sq(d-1)”, ${\chi}^{2}\left(8\right)$ test (9); “Mean”, normal test on the mean (1); “Mean & var.”, normal test on mean and variance (3); “Variance”, normal test on variance (2). The dashed line is $power=actual\phantom{\rule{0.277778em}{0ex}}size$. The number of observations is indicated on top of each panel.

**Figure 6.**First two digits tests: deviation of the actual size from the nominal size. Tests are as follows: “Adj. MAD”, adjusted MAD (10); “Chi-sq(2)”, ${\chi}^{2}\left(2\right)$ test on mean and variance (4); “Chi-sq(d-1)”, ${\chi}^{2}\left(89\right)$ test (9); “Mean”, normal test on the mean (1); “Mean & var.”, normal test on mean and variance (3); “Variance”, normal test on variance (2). The number of observations is indicated on top of each panel.

**Figure 7.**First two digits tests: size–power curves of the tests against the uniform mixing alternative with $\lambda =0.9$. Tests are as follows: “Adj. MAD”, adjusted MAD (10); “Chi-sq(2)”, ${\chi}^{2}\left(2\right)$ test on mean and variance (4); “Chi-sq(d-1)”, ${\chi}^{2}\left(89\right)$ test (9); “Mean”, normal test on the mean (1); “Mean & var.”, normal test on mean and variance (3); “Variance”, normal test on variance (2). The dashed line is $power=actual\phantom{\rule{0.277778em}{0ex}}size$. The number of observations is indicated on top of each panel.

**Figure 8.**First two digits tests: size–power curves of the tests against the normal mixing alternative with $\lambda =0.9$. Tests are as follows: “Adj. MAD”, adjusted MAD (10); “Chi-sq(2)”, ${\chi}^{2}\left(2\right)$ test on mean and variance (4); “Chi-sq(d-1)”, ${\chi}^{2}\left(89\right)$ test (9); “Mean”, normal test on the mean (1); “Mean & var.”, normal test on mean and variance (3); “Variance”, normal test on variance (2). The dashed line is $power=actual\phantom{\rule{0.277778em}{0ex}}size$. The number of observations is indicated on top of each panel.

**Figure 9.**First two digits tests: size–power curves of the tests against the perturbed mixing alternative with $\lambda =0.75$. Tests are as follows: “Adj. MAD”, adjusted MAD (10); “Chi-sq(2)”, ${\chi}^{2}\left(2\right)$ test on mean and variance (4); “Chi-sq(d-1)”, ${\chi}^{2}\left(89\right)$ test (9); “Mean”, normal test on the mean (1); “Mean & var.”, normal test on mean and variance (3); “Variance”, normal test on variance (2). The dashed line is $power=actual\phantom{\rule{0.277778em}{0ex}}size$. The number of observations is indicated on top of each panel.

**Figure 10.**First two digits tests: size–power curves of the tests against the rounding mixing alternative with $\lambda =0.75$. Tests are as follows: “Adj. MAD”, adjusted MAD (10); “Chi-sq(2)”, ${\chi}^{2}\left(2\right)$ test on mean and variance (4); “Chi-sq(d-1)”, ${\chi}^{2}\left(89\right)$ test (9); “Mean”, normal test on the mean (1); “Mean & var.”, normal test on mean and variance (3); “Variance”, normal test on variance (2). The dashed line is $power=actual\phantom{\rule{0.277778em}{0ex}}size$. The number of observations is indicated on top of each panel.

**Figure 11.**Average estimated $MAD$s over 1000 replications under the (Benford’s law) null hypothesis (blue points) and $\alpha /\sqrt{n}$ (black curve) for varying sample sizes $n\in (250,500,\dots ,10,000)$. $\alpha $ is a scale factor used to report $1/\sqrt{n}$ on the same scale as $MAD$. The shaded area represents the central 90% of the distribution of estimated $MAD$s. The horizontal dashed line corresponds to Nigrini’s suggested critical value (0.0022). The vertical dashed line corresponds to $n=1750$.

**Figure 12.**Behaviour of conformance tests across samples. In the first row (panels

**A**–

**C**), data conform to the “first two digits” Benford’s law. In the second row (panels

**D**–

**F**), data follow a perturbed Benford’s law with $\lambda =0.95$. In the third row (panels

**G**–

**I**), data are consistent with a perturbed Benford’s law with $\lambda =0.75$. The first column (panels

**A**,

**D**,

**G**) reports the results computed over the full sample, with n = 15,000. The second column (panels

**B**,

**E**,

**H**) is relative to a single random subsample with n = 1750. The third column (panels

**C**,

**F**,

**I**) reports the estimated densities (blue) of the conventional (or Wald) chi-square test statistic over 5000 random subsamples of length n = 1750 along with the ${\chi}^{2}\left(89\right)$ distribution under the null distribution (red). $P\left({\chi}_{89}^{2}\right)$ and $P(Adj.MAD)$ denote p values of the conventional (or Wald) chi-square test and of the adjusted $MAD$ test, respectively. $Prob.\phantom{\rule{0.277778em}{0ex}}of\phantom{\rule{0.277778em}{0ex}}sup.$ is an estimate of the probability of superiority.

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Cerqueti, R.; Lupi, C.
Some New Tests of Conformity with Benford’s Law. *Stats* **2021**, *4*, 745-761.
https://doi.org/10.3390/stats4030044

**AMA Style**

Cerqueti R, Lupi C.
Some New Tests of Conformity with Benford’s Law. *Stats*. 2021; 4(3):745-761.
https://doi.org/10.3390/stats4030044

**Chicago/Turabian Style**

Cerqueti, Roy, and Claudio Lupi.
2021. "Some New Tests of Conformity with Benford’s Law" *Stats* 4, no. 3: 745-761.
https://doi.org/10.3390/stats4030044