Comparison of the robustness of alternatives to the two sample test under non normality distributions through Monte Carlo simulation

• Autor(a) principal: Silva, Roberta Bessa Veloso
• Data de Publicação: 2008
• Outros Autores: Ferreira, Daniel Furtado
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Acta scientiarum. Technology (Online)
• Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/2554
• topic: probabilidade do erro tipo I; poder do teste; bootstrap; não normalidade; 1.02.02.00-5 Estatística

This work aimed to evaluate the risks of committing type I and type II errors in non normal populations by means of computational simulation and to compare three tests usually applied. It was compared the t test with the approach of the degrees of freedom proposed by Satterthwaite (1946), t with the degrees of freedom given by v = min (n1 - 1, n2 - 1) and bootstrap method under different distributions of probability. Under non normal distribution the t with Satterthwaite adjustment of degrees of freedom and with v = min (n1 - 1, n2 - 1) degrees of freedom did not control type I error probabilities. The bootstrap criterion controlled the type I error rates and presented equivalent power being considered robust with the violation of the normality assumption. The t test under non normal distribution with Satterthwaite adjustment of degrees of freedom with samples of different sizes presented type I error rates greater than the nominal levels.