Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Statistics review 9: One-way analysis of variance. | Critical Care April 2004 Vol 8 No 2 Bewick et al. Review Statistics review 9 One-way analysis of variance Viv Bewick1 Liz Cheek1 and Jonathan Ball2 Senior Lecturer School of Computing Mathematical and Information Sciences University of Brighton Brighton UK 2Lecturer in Intensive Care Medicine St George s Hospital Medical School London UK Correspondence Viv Bewick Published online 1 March 2004 Critical Care 2004 8 130-136 DOI cc2836 This article is online at http content 8 2 130 2004 BioMed Central Ltd Print ISSN 1364-8535 Online ISSN 1466-609X Abstract This review introduces one-way analysis of variance which is a method of testing differences between more than two groups or treatments. Multiple comparison procedures and orthogonal contrasts are described as methods for identifying specific differences between pairs of treatments. Keywords analysis of variance multiple comparisons orthogonal contrasts type I error Introduction Analysis of variance often referred to as ANOVA is a technique for analyzing the way in which the mean of a variable is affected by different types and combinations of factors. One-way analysis of variance is the simplest form. It is an extension of the independent samples t-test see statistics review 5 1 and can be used to compare any number of groups or treatments. This method could be used for example in the analysis of the effect of three different diets on total serum cholesterol or in the investigation into the extent to which severity of illness is related to the occurrence of infection. Analysis of variance gives a single overall test of whether there are differences between groups or treatments. Why is it not appropriate to use independent sample t-tests to test all possible pairs of treatments and to identify differences between treatments To answer this it is necessary to look more closely at the meaning of a P value. When interpreting a P value it can be concluded that there is a .
