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Statistical Concepts Series |
1 From the Department of Radiology, Riley Hospital for Children, Indianapolis, Ind (K.E.A.); Department of Radiology, Boston University, 88 E Newton St, Atrium 2, Boston, MA 02118 (R.T.); and Departments of Radiology and Biostatistics, Indiana University School of Medicine, Indianapolis (J.Y.). Received October 16, 2002; revision requested February 18, 2003; final revision received March 13; accepted April 1. Address correspondence to R.T. (e-mail: tello@alum.mit.edu).
Radiology research involves comparisons that deal with the presence or absence of various imaging signs and the accuracy of a diagnosis. In this article, the authors describe the statistical tests that should be used when the data are not distributed normally or when they are categorical variables. These nonparametric tests are used to analyze a 2 x 2 contingency table of categorical data. The tests include the 
2 test, Fisher exact test, and McNemar test. When the data are continuous, different nonparametric tests are used to compare paired samples, such as the Mann-Whitney U test (equivalent to the Wilcoxon rank sum test), the Wilcoxon signed rank test, and the sign test. These nonparametric tests are considered alternatives to the parametric t tests, especially in circumstances in which the assumptions of t tests are not valid. For radiologists to properly weigh the evidence in the literature, they must have a basic understanding of the purpose, assumptions, and limitations of each of these statistical tests.
© RSNA, 2003
Index terms: Statistical analysis
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