2018
DOI: 10.4025/actasciagron.v40i1.35300
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<b>Data transformation: an underestimated tool by inappropriate use

Abstract: There are researchers who do not recommend data transformation arguing it causes problems in inferences and mischaracterises data sets, which can hinder interpretation. There are other researchers who consider data transformation necessary to meet the assumptions of parametric models. Perhaps the largest group of researchers who make use of data transformation are concerned with experimental accuracy, which provokes the misuse of this tool. Considering this, our paper offer a study about the most frequent situ… Show more

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Cited by 26 publications
(15 citation statements)
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“…For the classical physiological measurements recorded from CRD, we tested the data for the assumptions (α = 0.01) of normality of residuals (Kolmogorov-Smirnov test) and homogeneity of variances (Levene test), and, when the assumptions were violated, we performed data transformation according to [36] (S3 Table). For cumulative germination curves, we chose regression models based on significance from ANOVA test and model fitting to observed data.…”
Section: Discussionmentioning
confidence: 99%
“…For the classical physiological measurements recorded from CRD, we tested the data for the assumptions (α = 0.01) of normality of residuals (Kolmogorov-Smirnov test) and homogeneity of variances (Levene test), and, when the assumptions were violated, we performed data transformation according to [36] (S3 Table). For cumulative germination curves, we chose regression models based on significance from ANOVA test and model fitting to observed data.…”
Section: Discussionmentioning
confidence: 99%
“…Relevance of Data Transformation Techniques … 86 assumptions are not met (Ribeiro-Oliveira et al 2018). A classic instance of improper employment of data transformation is the attempt to decrease the coefficient of variation (Souza et al 2008).…”
Section: Angular Transformationmentioning
confidence: 99%
“…On the contrary, non-parametric models can be extensively used to avoid the assumptions needed for parametric ANOVA. Ribeiro- Oliveira et al (2018) mentioned that non-parametric methods are applicable, especially when there are no residuals adjusting to the Gaussian distribution (Judice et al 1999). This seems to be quite conflicting because normal approximation for large samples is considered as a basis of the nonparametric statistics (Zar, 1999).…”
Section: Angular Transformationmentioning
confidence: 99%
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“…O processo germinativo das sementes foi estudado usando um modelo hierárquico As pressuposições do modelo foram verificadas pelo teste de Kolmogorov-Smirnov, para determinar a distribuição normal dos resíduos, e pelo teste de Levene, para homocedasticidade, ambos a 0,01 de significância. Toda vez que a transformação de dados não levou à ajustes da pressuposição, mas melhorou a probabilidade dos testes, optou-se pela estatística paramétrica (RIBEIRO-OLIVEIRA et al 2018). A comparação da significância do modelo se deu via teste F de Snedecor, e as comparações múltiplas das médias foram viabilizadas pelo teste de Tukey, ambos 0,05 de significância.…”
Section: Modelo Hierárquico Ou Análise De Nestedunclassified