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An Experimental Analysis of Children's Ability to Provide a False Report about a Crime

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Generalized Benford's Law as a Lie Detector.

Nicolas Gauvrit Gauvrit¹, Jean-Charles Houillon², Jean-Paul Delahaye³

¹Laboratoire Cognition Humaine et Artificielle, Ecole Pratique des Hautes Etudes, Les Patios Saint-Jacques, Paris, France.

Advances in Cognitive Psychology

|July 18, 2017

Summary

This summary is machine-generated.

Benford's law describes the distribution of first digits in real-world data. This study validates a generalized Benford analysis for detecting fabricated data, showing it is effective even with varying familiarity and cognitive effort.

Keywords:

Benford analysis fraud detection generalized Benford’s law

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Area of Science:

Data Science
Statistics
Psychology

Background:

Benford's Law describes a logarithmic distribution of first digits in naturally occurring datasets.
Deviations from Benford's Law can indicate data fabrication.
A recent generalization, Generalized Benford's Law (GBL), extends this analytical framework.

Purpose of the Study:

To provide empirical support for the Generalized Benford Analysis (GBL).
To broaden the application of Benford analysis for detecting fabricated data.
To assess the impact of domain familiarity and cognitive effort on GBL accuracy.

Main Methods:

Empirical testing of the Generalized Benford Analysis across two studies.
Analysis of numerical datasets to evaluate conformity to GBL.
Comparison of results with varying levels of subject familiarity and cognitive load.

Main Results:

The Generalized Benford Analysis demonstrated empirical support, broadening classical Benford analysis.
The method proved effective in identifying deviations indicative of fabricated data.
Domain familiarity and cognitive effort had minimal impact on the accuracy of the analysis.

Conclusions:

The Generalized Benford Analysis is a robust tool for detecting fabricated data.
The effectiveness of Benford analysis is not significantly hindered by factors like familiarity or cognitive effort.
This research expands the utility of Benford's Law in data integrity verification.