<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2007-5936</journal-id>
<journal-title><![CDATA[Psicumex]]></journal-title>
<abbrev-journal-title><![CDATA[Psicumex]]></abbrev-journal-title>
<issn>2007-5936</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Sonora, Consorcio de Universidades Mexicanas A,C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-59362024000100118</article-id>
<article-id pub-id-type="doi">10.36793/psicumex.v14i1.648</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Exposición Sencilla y Ejemplificada de la Ley de Newcomb-Benford para Psicólogos]]></article-title>
<article-title xml:lang="en"><![CDATA[Simple and Exemplified Exposition of the Newcomb-Benford Law for Psychologists]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Moral de la Rubia]]></surname>
<given-names><![CDATA[José]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Nuevo León  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<volume>14</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-59362024000100118&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-59362024000100118&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-59362024000100118&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen 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.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract 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.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Ley Newcomb-Benford]]></kwd>
<kwd lng="es"><![CDATA[ley de los primeros dígitos]]></kwd>
<kwd lng="es"><![CDATA[distribución discreta]]></kwd>
<kwd lng="es"><![CDATA[bondad de ajuste]]></kwd>
<kwd lng="es"><![CDATA[intervalos de confianza simultáneos]]></kwd>
<kwd lng="en"><![CDATA[Newcomb-Benford law]]></kwd>
<kwd lng="en"><![CDATA[first digit law]]></kwd>
<kwd lng="en"><![CDATA[discrete distribution]]></kwd>
<kwd lng="en"><![CDATA[goodness of fit]]></kwd>
<kwd lng="en"><![CDATA[simultaneous confidence intervals]]></kwd>
</kwd-group>
</article-meta>
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