Benford's Law First-Digit Checker
Count leading nonzero decimal digits 1–9 in nonzero observations for comparison with Benford's logarithmic proportions. A small or range-restricted sample alone is not evidence of fraud.
Description
Count leading nonzero decimal digits 1–9 in nonzero observations for comparison with Benford's logarithmic proportions. A small or range-restricted sample alone is not evidence of fraud.
Benford's Law First-Digit Checker is a focused tool for the following task. Count leading nonzero decimal digits 1–9 in nonzero observations for comparison with Benford's logarithmic proportions. A small or range-restricted sample alone is not evidence of fraud. It reports Counts for digits 1–9, Observation count from the values you provide rather than inventing measurements, coefficients, or professional judgment that are not part of the input.
When to use Benford's Law First-Digit Checker
Use this calculation to reproduce a defined quantitative method when the observations, units, sampling process, and assumptions match the method shown here.
- Observations
- Required list.
The cited overview of Statistics supplies background for the terminology and domain context used by this tool.1
How Benford's Law First-Digit Checker works
Count leading nonzero decimal digits 1–9 in nonzero observations for comparison with Benford's logarithmic proportions. A small or range-restricted sample alone is not evidence of fraud. Inputs are interpreted exactly in the displayed units and the calculation returns the following fields without presentation rounding.
- Counts for digits 1–9
- Returned list.
- Observation count
- Returned integer.
Limitations and assumptions
- A numerical result does not by itself establish data quality, causation, representativeness, independence, distributional fit, or practical significance.
- Use finite inputs in the displayed units, preserve source measurements and assumptions, and independently verify consequential decisions.
Alternative or Complementary approaches
Inspect the underlying data, visualize its distribution, report uncertainty and sample size, and compare the result with a robust or domain-specific method where appropriate.
References
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Statistics — Wikipedia contributors
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