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Arch Hellen Med, 22(4), July-August 2005, 377-391


Data analysis: Non Bayesian approach

Laboratory of Clinical Epidemiology, School of Nursing, University of Athens, Athens, Greece

An epidemiologic study can be viewed as an exercise in measurement. As in any measurement, the goal is to obtain an accurate result, with as little error as possible. There are three types of error; random error, systematic error and confounding. Random error is nothing other than variability in the data that cannot be readily explained. If an epidemiologic study is thought of as an exercise in measurement, then the result should provide, usually, an estimate of a measure of effect. Ideally, the analysis of data and the reporting of results should quantify the magnitude of that measure of effect and portray the degree of precision with which it is measured. Statistical estimation provides these quantities. When an estimate is presented as a single value, it is referred to as a point estimate. To indicate the precision of the point estimate, a confidence interval is used, which is a range of values around the point estimate. A wide confidence interval indicates low precision and a narrow interval indicates high precision. The reason for the use of confidence intervals is that a point estimate, being a single value, cannot express the statistical variation or random error that underlies the estimate. Confidence intervals are used to indicate the amount of random error in the estimate.

Key words: Confidence interval, Point estimate, Random error, Systematic error.

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