Assumptions for Statistical Tests As we can see throughout this website, most of the statistical tests we perform are based on a set of assumptions. When these assumptions are violated the results of the analysis can be misleading or completely erroneous. Typical assumptions are: Normality: Data have a normal distribution (or at least is symmetric) Homogeneity of variances: Data from multiple groups have the same variance Linearity: Data have a linear relationship Independence: Data are independent We explore in detail what it means for data to be normally distributed in Normal Distribution, but in general it means that the graph of the data has the shape of a bell curve. Such data is symmetric around its mean and has kurtosis equal to zero. In Testing for Normality and Symmetry we provide tests to determine whether data meet this assumption. Some tests (e.g. ANOVA) require that the groups of data being studied have the same variance. In Homogeneity of Variances we provide some...
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