Two categories of measurement error. Accuracy refers to how close measurements are to the "true" value, while precision refers to how close measurements are to each other. Accuracy and precision are usually evaluated through various measurement system analysis tools, such as Gage R&R Studies.
Within any measurement system, you can have one or both of these types of problems. For example, suppose you are producing 200mg medication tablets. In your sampling, you test the weight of these tablets. You can have a device that measures the tablets precisely (little variation in the measurements) but not accurately- Measurements of a single tablet may be 205.54mg, 205.43mg, 205.03mg. You can also have a device that is accurate (the average of the measurements is very close to the accurate value), but not precise. That is, the measurements have large variance: 198.54mg, 202.78mg, 200.33mg. You can also have a device that is neither accurate nor precise.
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Accurate and precise |
Precise but not accurate |
Accurate but not precise |
Not accurate or precise |
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Measurements are close to the true value and are close to each other. |
Measurements are close to each other but not close to the true value. |
Measurements are close to the true value but not to each other. |
Measurements are not close to the true value or to each other. |
The accuracy of a measurement system has three components: bias, linearity, and stability. The precision of a measurement system has two components: repeatability and reproducibility. These components can be studied in more detail with various gage studies.
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Note |
The integrity of your data depends upon the integrity of your measurement system. If you detect problems with accuracy and precision, you must improve the measurement system before you can rely upon your data. |
