Understanding Accuracy and Precision in Measurement Systems
What is Accuracy?
Accuracy refers to how close a measured value is to the true or standard value. It indicates the reliability of a measurement system in providing correct results.
Example of Accuracy:
Consider a ball that has a true weight of 100 grams. If three measurements are taken and the readings are:
✅ 99.9 gm
✅ 99.8 gm
✅ 100.1 gm
These values are very close to the actual weight, meaning the measurement system is accurate.
Key Point: Every measurement system should be accurate to ensure reliable data for decision-making.
Measurement System Analysis (MSA)
To improve a measurement system, we need to evaluate both accuracy and precision. Below are the three key factors that influence accuracy.
1. Bias
Definition: Bias is a systematic error that consistently skews measurements in one direction. It represents the difference between the measured average and the actual value.
Examples of Bias:
How to Calculate Bias?
Bias is calculated as:
Bias = Measured Average – True Value
2. Linearity
Definition: Linearity refers to a measurement system’s accuracy across its entire range.
Example of Linearity:
Imagine a weighing scale used for:
✅ A baby (lightweight)
✅ A child
✅ An adult
✅ A large person (heavyweight)
If the scale provides consistent accuracy across all weight ranges, it has good linearity.
3. Stability
Definition: Stability means that repeated measurements of the same object at different times give consistent results.
Example of Stability:
✅ If you weigh an object today and get 100 gm, then measure it the next day and still get 100 gm, the system is stable.
Note: Accuracy is not only dependent on tools but also on operators and environmental conditions.
What is Precision?
Precision refers to the closeness of multiple measured values, even if they are far from the actual value.
Example of Precision:
If the real weight of a ball is 100 gm, and three readings are:
✅ 97.5 gm
✅ 97.8 gm
✅ 97.6 gm
The values are close to each other but not close to 100 gm, meaning the system is precise but not accurate.
How to Achieve Precision?
To ensure precision, we must evaluate two key factors:
1. Repeatability
Definition: Repeatability refers to the ability of the same operator using the same instrument to get consistent results under the same conditions.
2. Reproducibility
Definition: Reproducibility means that different operators using the same measurement tool can achieve similar results under the same conditions.
Accuracy vs. Precision: What’s the Difference?
| Feature | Accuracy | Precision |
|---|---|---|
| Definition | Closeness to the true value | Closeness of repeated values |
| Reliability | Ensures correct measurement | Ensures consistency |
| Example | Readings close to 100 gm when actual weight is 100 gm | Readings close to each other but far from 100 gm |
- Accurate & Precise: Shots are close together and near the center.
- Accurate but Not Precise: Shots are near the center but spread out.
- Precise but Not Accurate: Shots are close together but away from the center.
- Neither Accurate nor Precise: Shots are scattered randomly.
Final Thoughts
A valid measurement system should be both accurate and precise. If a system lacks accuracy, it provides incorrect results. If it lacks precision, results will be inconsistent.
To ensure a reliable measurement system, we must assess:
✅ The tools used for measurement
✅ The operators handling the tools
✅ The environmental conditions affecting measurements
By improving accuracy and precision, we can make better data-driven decisions in any industry.
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