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Radiology. 2003 Feb;226(2):297-301.

Measurement variability and confidence intervals in medicine: why should radiologists care?

Author information

1
Department of Radiology and Health Outcomes Policy and Economics (H.O.P.E.) Center, Children's Hospital, 3100 SW 62nd Ave, Miami, FL 33155, USA. smedina@post.harvard.edu

Abstract

In radiology, appropriate diagnoses are often based on quantitative data. However, these data contain inherent variability. Radiologists often see P values in the literature but are less familiar with other ways of reporting statistics. Statistics such as the SD and standard error of the mean (SEM) are commonly used in radiology, whereas the CI is not often used. Because the SEM is smaller than the SD, it is often inappropriately used in order to make the variability of the data look tighter. However, unlike the SD, which quantifies the variability of the actual data for a single sample, the SEM represents the precision for an estimated mean of a general population taken from many sample means. Since readers are usually interested in knowing about the variability of the single sample, the SD often is the preferred statistic. Statistical calculations combine sample size and variability (ie, the SD) to generate a CI for a population proportion or population mean. CIs enable researchers to estimate population values without having data from all members of the population. In most cases, CIs are based on a 95% confidence level. The advantage of CIs over significance tests (P values) is that the CIs shift the interpretation from a qualitative judgment about the role of chance to a quantitative estimation of the biologic measure of effect. Proper understanding and use of these fundamental statistics and their calculations will allow more reliable analysis, interpretation, and communication of clinical information among health care providers and between these providers and their patients.

PMID:
12563116
DOI:
10.1148/radiol.2262011537
[Indexed for MEDLINE]

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