Abstract
A good place to start looking at your data before analysis is a data plot, e.g., a scatter plot or histogram. It can help you decide whether the data are normal (bell shape, Gaussian), and give you a notion of outlier data and skewness. Another approach is using a normality test like the chi-square goodness of fit, the Shapiro-Wilkens, or the Kolmogorov Smirnov tests (see Testing clinical trials for randomness, Chap. 42, in: Statistics applied to clinical studies 5th edition, Springer Heidelberg Germany, 2012, from the same authors), but these tests often have little power, and, therefore, do not adequately identify departures from normality. This chapter is to assess the performance of another and probably better method, the Q-Q (quantile-quantile) plot.
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Cleophas, T.J., Zwinderman, A.H. (2020). Quantile-Quantile Plots, a Good Start for Looking at your Medical Data (50 Cholesterol Measurements and 58 Patients). In: Machine Learning in Medicine – A Complete Overview. Springer, Cham. https://doi.org/10.1007/978-3-030-33970-8_43
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DOI: https://doi.org/10.1007/978-3-030-33970-8_43
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