Accuracy and precision are terms often used in measurement, statistics, and science, but they have distinct meanings:
1. Accuracy
Definition: Accuracy refers to how close a measured value is to the true or accepted value.
- Key Points:
- It indicates the correctness of a measurement.
- A measurement system is accurate if it produces results that are close to the real value.
- Accuracy is influenced by systematic errors, such as instrument calibration or measurement technique.
Example: If the actual weight of an object is 10 kg and a scale shows readings of 9.8 kg, 10.0 kg, and 10.2 kg, the scale is accurate because the measurements are close to the true value.
2. Precision
Definition: Precision refers to how close repeated measurements are to each other, regardless of whether they are close to the true value.
- Key Points:
- It indicates the consistency or repeatability of a measurement.
- A measurement system is precise if it produces similar results under the same conditions.
- Precision is influenced by random errors, such as environmental fluctuations or inconsistent techniques.
Example: If the same scale consistently shows readings of 9.5 kg, 9.6 kg, and 9.7 kg for an object that weighs 10 kg, the scale is precise but not accurate.
Visualizing Accuracy vs. Precision
Imagine a dartboard:
- Accurate but Not Precise: Darts are close to the bullseye but scattered.
- Precise but Not Accurate: Darts are clustered together but far from the bullseye.
- Accurate and Precise: Darts are both close to the bullseye and clustered.
- Neither Accurate Nor Precise: Darts are scattered and far from the bullseye.
Key Differences
| Aspect | Accuracy | Precision |
|---|---|---|
| Definition | Closeness to the true value | Closeness of repeated measurements to each other |
| Error Type | Affected by systematic errors | Affected by random errors |
| Focus | Correctness of results | Consistency of results |
| Evaluation | Single measurement compared to true value | Multiple measurements compared to each other |
Combined Importance
For a system to be effective in measurement or analysis, it should ideally be both accurate and precise, ensuring that results are correct and consistent.