title: [Data transformation and normalization].
abstract: When we analyze measured values by statistical techniques, we usually use a parametric method. It is necessary for measured values to show a normal distribution. Therefore, we must confirm that the distribution is normal, whereby a histogram shows that the distribution of data points is symmetrical above and below the mean. When measured values do not show a normal distribution, power transformation (square root transformation, logarithmic transformation) must be performed. We can evaluate the presence of normality by three methods: viewing a histogram, through skewness and kurtosis values, and Kolmogorov-Smirnov method's p-value. Because Kolmogorov-Smirnov's method is influenced by outliers, attention is necessary regarding the interpretation of p-values. For example, if we calculate reference intervals from clinical testing data, we calculate a parameter(mean and standard deviation) and set X +/- 2SD to upper and lower limits, respectively. When we evaluate the reference intervals, a range including 95% of the central part of sample is important. Identification of the distribution type based on the diagonal linear pattern of normal quantile plots may be the most reliable in my experience.
authors.affiliation: Biomedical Laboratory Sciences, Institute of Health Biosciences, The University of Tokushima Graduate School, Tokushima 770-8509, Japan. nishida@medsci.tokushima-u.ac.jp
authors.forename: Toshinobu
authors.initials: T
authors.lastname: Nishida
mesh_terms: D016011:Normal Distribution; D013223:Statistics as Topic