Glossary
Measurement error
What is measurement error?
Measurement error is the difference between the measured value and the true value of a variable. All measurements contain some error, and understanding its magnitude and direction is essential for interpreting research findings and making valid inferences.
How it works
Measurement error comes in two forms: random error (unpredictable fluctuations that add noise and can also bias estimated associations, depending on where the error occurs) and systematic error (consistent distortion in one direction that biases results).
The consequences depend on whether error affects an exposure, outcome, or adjustment variable, and on its relation to the other variables. Hernán and Robins’s discussion of measurement explains why measurement error can move an association toward or away from the null. Self-report measures are especially prone to systematic error through social desirability bias, memory distortion, and response styles.
Applied example
Suppose participants overreport physical activity on a questionnaire. An intervention estimate could be distorted if reporting differs between groups or changes over time. The direction is not determined by an inflated baseline alone; a device measure also needs its own validity assessment.
Why it matters
Better measurement and validation data can strengthen inference. A correction is only as useful as what we know about the error. Calling a measure objective or validated does not settle how it performed in this study.