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 but do not systematically bias results) and systematic error (consistent distortion in one direction that biases results). Random error reduces statistical power (making true effects harder to detect) while systematic error produces misleading conclusions. Self-report measures are especially prone to systematic error through social desirability bias, memory distortion, and response styles.
Applied example
A study measuring physical activity through self-report finds people overestimate their exercise by 40% compared to accelerometer data. This systematic measurement error means that interventions appear less effective than they are (because the inflated baseline leaves less room for improvement).
Why it matters
Measurement error determines the ceiling on what a study can validly conclude: no analysis can be more accurate than the data it is based on.
