Measurement

David L. McArthur

Scientists from numerous disciplines frequently make sense of the world by using yardsticks that they hope will show how their study participants are performing, what they are thinking, and how they interact with others. Numbers are faithfully recorded, spun through various forms of software, and prepared for publication. All this is fine if the yardsticks themselves are true—all the time, in every single place they are used, regardless of who is doing the actual recording of the numbers, and regardless of the circumstances in which the numbers are obtained. But what if the yardsticks themselves are shaky? In epidemiologic analyses based strictly on counting, a few units in dispute here or there may seem rather unlikely to significantly change the overall interpretation of the data set. However, even a single reclassification of a case from one cell to another, for instance, can force a confidence interval to bracket 1.0 where ...

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