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Measurement is never perfect, and we can always expect measurement errors in our data. Our goal, of course, is to keep these errors to a minimum. For this reason, we need to be aware of the various sources and causes of measurement error. Note 3
Random error is a nonsystematic measurement error that is beyond our control, though its effects average out over a set of measurements. For example, a scale may be properly calibrated but give inconsistent weights (sometimes too high, sometimes too low). Over repeated uses, however, the effects of these random errors average out to zero. The errors are random rather than biased: They neither understate nor overstate the actual measurement.
In contrast, measurement bias, or systematic error, favors a particular result. A measurement process is biased if it systematically overstates or understates the true value of the measurement. Consider our scale example again. If a scale is not properly calibrated, it might consistently understate weight. In this case, the measuring device -- the scale -- produces the bias. Human observation can also produce bias. The important thing to keep in mind is that biased measurements invariably produce unreliable results.
In any statistical investigation, we can always attribute some of the variation in data to measurement error, part of which can result from the measurement instrument itself. But human mistakes, especially recording errors (e.g., misreading a dial, incorrectly writing a number, not observing an important event, misjudging a particular behavior), can also often contribute to the variability of the measurement and thus to the results of a study.
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