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Third, with small sample size we have to be prepared to make stronger model- ling assumptions. For example, Altman illustrates the use of a parametric test (ANOVA) to compare 3 groups with 8, 9, and 5 patients in his seminal text “Practical statistics for medical research”. 8 With larger samples, we would more readily switch to a non-parametric test such as a Kruskal–Wallis test. With small sample size, we may have to assume linearity of a continuous predictor (Chap. 9) and no interaction between predictors (Chap. 13). We will subsequently have lim- ited power to test deviations from these model assumptions. It hence becomes more important what our starting point of the analysis is. From a Bayesian viewpoint, we could say that our prior information becomes more important, since the information contributed by our study is limited
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