Exposición Sencilla y Ejemplificada de la Ley de Newcomb-Benford para Psicólogos

• Autores: José Moral de la Rubia
• Localización: PSICUMEX, ISSN-e 2007-5936, Nº. 14, 2024 (Ejemplar dedicado a: Psicumex Journal (January - December 2024)), págs. 1-35
• Idioma: español
• DOI: 10.36793/psicumex.v14i1.648
• Títulos paralelos:
  • Simple and Exemplified Exposition of the Newcomb-Benford Law for Psychologists
• Enlaces
  • Texto completo
• Resumen
  • español

    Este artículo metodológico tiene como objetivo exponer la Ley de Newcomb-Benford de una forma clara, acompañada de un ejemplo, para facilitar su comprensión entre diversas áreas de investigación psicológica ajenas a su uso en otras disciplinas, incluida la ciencia cognitiva. Se aplica sobre todo a la detección del fraude en bases de datos y escrutinio electoral. Este artículo inicia con una reseña histórica, presenta las distribuciones del primer al cuarto dígito significativo y la de dos dígitos. Se revisan las explicaciones estadístico-matemáticas de la ley. Se presentan de forma aplicada seis pruebas de bondad de ajuste y el cálculo de intervalos de confianza simultáneos para comprobar el cumplimiento de la ley. Se usan datos simulados que siguen dos distribuciones: normal y lognormal. La primera, común en psicología, no se ajusta a la ley, mientras que la segunda posibilita transformar la distribución normal para cumplirla. Finalmente, se extraen conclusiones y se plantean sugerencias para detectar manipulación de datos normalmente distribuidos.

  • English

    The purpose of this methodological article is to clearly present the Newcomb-Benford law, accompanied by an example, to enhance understanding among diverse areas of research in psychology unfamiliar with its use in other disciplines, including cognitive science. This law is primarily applied for detecting fraud in databases and tallying votes in popular elections. The article commences with a historical overview, presenting distributions from the first to the fourth significant digit, as well as the two-digit distribution. Statistical-mathematical explanations of the law are reviewed, followed by the presentation of six goodness-of-fit tests and the calculation of simultaneous confidence intervals to assess compliance with the law. Simulated data following two distributions, namely normal and lognormal, are employed. The former, common in psychology, doesn't conform to the law, while the latter facilitates transforming the normal distribution to adhere to it. Finally, conclusions are drawn, and suggestions are made to detect manipulation of normally distributed data.

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