Measurement error refers to the difference between the measured value and the true value of what you’re trying to measure. It reflects the degree to which a measurement tool or process produces results that are not perfectly accurate. All measurements contain some level of error, whether you’re measuring physical, psychological, or behavioral properties.
Measurement error can be divided into two main types:
- Random: Unpredictable fluctuations that occur by chance and average out over time. These can arise from things like temporary distractions, slight variations in procedure, or environmental conditions.
- Systematic: Consistent, repeatable bias in the measurement process. These errors skew results in a particular direction and often stem from poorly calibrated instruments, flawed survey questions, or biased observation methods.
Learn more about Random vs. Systematic Error.
Measurement error reduces the reliability and validity of data. Minimizing error is essential for drawing meaningful conclusions, especially in scientific research, testing, and data analysis.
Measurement system analysis (MSA) is a structured approach for evaluating the presence, magnitude, and sources of measurement error in a system. It helps analysts determine whether the measurement process is reliable and accurate enough to support sound decision-making.
For example, a bathroom scale consistently shows weights that are 3 pounds too high. That’s a systematic error. If someone steps on the scale multiple times and sees slightly different readings each time due to shifting, that’s random error.
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