2003
DOI: 10.1155/s1173912603000178
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Robustness to non-normality of common tests for the many-sample location problem

Abstract: This paper studies the effect of deviating from the normal distribution assumption when considering the power of two many-sample location test procedures: ANOVA (parametric) and Kruskal-Wallis (non-parametric). Power functions for these tests under various conditions are produced using simulation, where the simulated data are produced using MacGillivray and Cannon's [10] recently suggested g-and-k distribution. This distribution can provide data with selected amounts of skewness and kurtosis by varying two nea… Show more

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Cited by 115 publications
(63 citation statements)
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“…Within groups, the index data were not normally distributed, frequently showing skewed or multimodal distributions, due to the presence of green and dying vegetation in oiled pixels (Figure 3). Parametric tests can be robust for skewed data and moderate deviations from normality, but are affected by kurtosis [56]. The Kruskal-Wallis test [55] is insensitive to both skewness and kurtosis [56] and can be used as a measure of the difference between group medians as long as the two distributions being compared have similar variances and distributions [57].…”
Section: Comparison Methods For Cumulative Impact Of Oil and Hurricanmentioning
confidence: 99%
See 1 more Smart Citation
“…Within groups, the index data were not normally distributed, frequently showing skewed or multimodal distributions, due to the presence of green and dying vegetation in oiled pixels (Figure 3). Parametric tests can be robust for skewed data and moderate deviations from normality, but are affected by kurtosis [56]. The Kruskal-Wallis test [55] is insensitive to both skewness and kurtosis [56] and can be used as a measure of the difference between group medians as long as the two distributions being compared have similar variances and distributions [57].…”
Section: Comparison Methods For Cumulative Impact Of Oil and Hurricanmentioning
confidence: 99%
“…Parametric tests can be robust for skewed data and moderate deviations from normality, but are affected by kurtosis [56]. The Kruskal-Wallis test [55] is insensitive to both skewness and kurtosis [56] and can be used as a measure of the difference between group medians as long as the two distributions being compared have similar variances and distributions [57]. Figure 3 shows that this is true for our between-group comparisons.…”
Section: Comparison Methods For Cumulative Impact Of Oil and Hurricanmentioning
confidence: 99%
“…Estas dos pruebas son robustas, de forma que garantizan la certeza de sus resultados, incluso con desviaciones al supuesto de normalidad, tal como se presentó en algunas de las variables analizadas en este estudio, aunque debe tenerse precaución con las inferencias realizadas. Ambas pruebas se consideran como la mejor opción para el análisis de datos, incluso con grupos de n < 5 frente a pruebas no paramétricas (Khan & Rayner, 2003;De Winter, 2013 …”
Section: Análisis De Datosunclassified
“…Valores de η 2 < .015 reflejan un tamaño de efecto trivial, de .015 a .124 pequeño, de .125 a .254 mediano, ≥ .255 grande (Ellis, 2010). Se optó por una prueba paramétrica al ser robusta al incumplimiento de normalidad con variable numérica con distribución acampanada sin apuntamiento excesivo (Khan & Rayner, 2003).…”
Section: Análisis De Datosunclassified