Benford’s law states that in listings, tables of statistics, etc., the digit 1 tends to occur with probability
, much greater than the expected 11.1% (i.e., one digit out of 9). More specifically, the empirical distribution of probabilities is
|

|

|

|

|
|
1 |
0.30103 |
6 |
0.0669468 |
|
2 |
0.176091 |
7 |
0.0579919 |
|
3 |
0.124939 |
8 |
0.0511525 |
|
4 |
0.09691 |
9 |
0.0457575 |
|
5 |
0.0791812 |
|
|
Using the suggested categories, we have the following table:
|
Category 1 |
Category 2 |
Category 3 |
|
Frequency |
1 |
1 |
22 |
We want to test the following:

So we have now the table with the expected values (under the null hypothesis):
|
Category 1 |
Category 2 |
Category 3 |
|
Frequency |
1 |
1 |
22 |
|
Expected |
7 |
12 |
5 |
We compute now the Chi-Square statistics:

The critical value for 2 df and
is 5.991. The test statistics is way beyond the critical value, so we reject the null hypothesis. This supports the claim the check amounts are the result of fraud.
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