Simulation Study on the Power and Sensitivity of Sixteen Normality Tests Under Different Non-Normality Scenarios
| dc.creator | Correa-Ávarez, Cristian David | |
| dc.creator | Rojas-Mora, Jessica María | |
| dc.creator | Zumaqué Ballesteros, Antonio Elías | |
| dc.creator | Bru-Cordero, Osnamir Elias | |
| dc.date | 2025-03-31 | |
| dc.date.accessioned | 2025-10-01T23:53:16Z | |
| dc.description | In data analysis, validating the normality assumption is crucial for determining the suitability of applying parametric methods. The objective of this research was to compare the power and sensitivity of sixteen normality tests, classified according to various aspects. The methodology involved simulating data using the Fleishman contamination system. This approach allowed us to evaluate the tests under non-normality conditions across ten distributions with varying degrees of deviation from normality. The results obtained showed that tests based on correlation and regression, such as Shapiro-Wilk and Shapiro-Francia, outperform the others in power, especially for large samples and substantial deviations from normality. For moderate deviations, the D’Agostino-Pearson and skewness tests performed well, while for low deviations, the Robust Jarque-Bera and Jarque-Bera tests were the most effective. Additionally, some tests exhibited high power across multiple distribution types, such as Snedecor-Cochran and Chen-Ye, which performed well for both symmetric platykurtic and asymmetric leptokurtic distributions. These findings offer valuable insights for selecting appropriate normality tests based on sample characteristics, which improves the reliability of statistical inference. Finally, it is concluded that this research demonstrates scenarios in which the most commonly used statistical tests are not always the most effective. | en-US |
| dc.description | En el análisis de datos, la validación del supuesto de normalidad es crucial para determinar si es correcto aplicar métodos paramétricos. El objetivo de esta investigación fue comparar la potencia y sensibilidad de dieciséis pruebas de normalidad, clasificadas según diversos aspectos. La metodología utilizada consistió en simular datos a partir del sistema de contaminación Fleishman para evaluar las pruebas en situaciones de no normalidad y diez distribuciones con distintos grados de desviación de la normalidad. Los resultados obtenidos fueron que las pruebas basadas en la correlación y la regresión, como Shapiro-Wilk y Shapiro-Francia, superaron a las demás en potencia, especialmente, para muestras grandes y desviaciones sustanciales de la normalidad. Para desviaciones moderadas se observó que las pruebas de D’Agostino-Pearson y de sesgo se desempeñaron bien, mientras que, para desviaciones bajas, sobresalieron la prueba robusta de Jarque-Bera y la prueba de Jarque-Bera. Además, algunas pruebas mostraron una elevada potencia en distintos tipos de distribuciones, como Snedecor-Cochran y Chen-Ye para distribuciones platicurticas simétricas, y Snedecor-Cochran y Chen-Ye para distribuciones leptocurticas asimétricas. Estos resultados aportaron información valiosa sobre la selección de pruebas de normalidad adecuadas en función de las características de la muestra, lo que ayuda a los investigadores a mejorar la fiabilidad de la inferencia estadística. En conclusión, este artículo muestra escenarios donde las pruebas estadísticas más conocidas no siempre son las más efectivas. | es-ES |
| dc.format | application/pdf | |
| dc.format | text/xml | |
| dc.format | application/zip | |
| dc.format | text/html | |
| dc.identifier | https://revistas.itm.edu.co/index.php/tecnologicas/article/view/3293 | |
| dc.identifier | 10.22430/22565337.3293 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12622/7934 | |
| dc.language | eng | |
| dc.publisher | Instituto Tecnológico Metropolitano (ITM) | es-ES |
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| dc.relation | https://revistas.itm.edu.co/index.php/tecnologicas/article/view/3293/3766 | |
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| dc.rights | Derechos de autor 2025 TecnoLógicas | es-ES |
| dc.rights | https://creativecommons.org/licenses/by-nc-sa/4.0 | es-ES |
| dc.source | TecnoLógicas; Vol. 28 No. 62 (2025); e3293 | en-US |
| dc.source | TecnoLógicas; Vol. 28 Núm. 62 (2025); e3293 | es-ES |
| dc.source | 2256-5337 | |
| dc.source | 0123-7799 | |
| dc.subject | método de clasificación de distribuciones | es-ES |
| dc.subject | método de Fleishman | es-ES |
| dc.subject | simulación Monte Carlo | es-ES |
| dc.subject | pruebas de normalidad | es-ES |
| dc.subject | comparación de potencias | es-ES |
| dc.subject | distribution classification method | en-US |
| dc.subject | Fleishman’s method | en-US |
| dc.subject | Monte Carlo simulation | en-US |
| dc.subject | normality tests | en-US |
| dc.subject | power comparison | en-US |
| dc.title | Simulation Study on the Power and Sensitivity of Sixteen Normality Tests Under Different Non-Normality Scenarios | en-US |
| dc.title | Estudio de simulación sobre la potencia y sensibilidad de dieciséis pruebas de normalidad en distintos escenarios de no normalidad | es-ES |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
| dc.type | Research Papers | en-US |
| dc.type | Artículos de investigación | es-ES |
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