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Hypothesis Testing II: Means¹

¹ From the Department of Radiology, Boston University School of Medicine, 88 E Newton St, Atrium 2, Boston, MA 02118 (R.T.); and Health Services Research and Development Service, Department of Veterans Affairs, Washington, DC (P.E.C.). Received February 11, 2002; revision requested March 18; revision received April 1; accepted May 1. Address correspondence to R.T. (e-mail: tello@alum.mit.edu).

Whenever means are reported in the literature, they are likely accompanied by tests to determine statistical significance. The t test is a common method for statistical evaluation of the difference between two sample means. It provides information on whether the means from two samples are likely to be different in the two populations from which the data originated. Similarly, paired t tests are common when comparing means from the same set of patients before and after an intervention. Analysis of variance techniques are used when a comparison involves more than two means. Each method serves a particular purpose, has its own computational formula, and uses a different sampling distribution to determine statistical significance. In this article, the authors discuss the basis behind analysis of continuous data with use of paired and unpaired t tests, the Bonferroni correction, and multivariate analysis of variance for readers of the radiology literature.

© RSNA, 2003

Index terms: Statistical analysis

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