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Assessing the validity of a statistical distribution: some illustrative examples from dermatological research
Author(s) -
Sürücü B.,
Koç E.
Publication year - 2008
Publication title -
clinical and experimental dermatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 78
eISSN - 1365-2230
pISSN - 0307-6938
DOI - 10.1111/j.1365-2230.2007.02629.x
Subject(s) - goodness of fit , quantile , normality , statistics , statistic , normal distribution , statistical hypothesis testing , mathematics , test statistic , econometrics , normality test
Summary Background. Assuming a statistical distribution is one of the key points before conducting a statistical analysis. Goodness‐of‐fit tests are used to assess the validity of an assumed statistical distribution. In dermatological research, the goodness‐of‐fit tests used are less powerful. Aim. We recommend the use of some specific goodness‐of‐fit tests for various distributions. A graphical technique called quantile–quantile plotting is introduced as an additional tool to assess the validity of an assumed distribution. We show why one should be careful in selecting a goodness‐of‐fit method by giving some relevant examples. Methods. Goodness‐of‐fit tests for testing normal and non‐normal distributions are introduced. Quantile–quantile plots were constructed, and we conducted a simulation study for testing normality. Results. We found that the Shapiro–Wilk statistic is the most powerful test overall to test for normal distribution. Quantile–quantile plotting is a very effective graphical technique to identify a distribution for a dataset. Conclusion. The use of the Shapiro–Wilk test and quantile–quantile plotting is recommended for testing normality.