Written By Adeel Abbas
Accuracy and precision are two important concepts in scientific measurement that are often used interchangeably, but actually have distinct meanings. In any measurement, it is important to understand how close the measured values are to the true value, as well as how close they are to each other.
Accuracy and precision are two measures that help scientists evaluate the quality of their measurements and ensure that they are generating reliable and meaningful data. In this article, we will explore the differences between accuracy and precision.
Here are the differences between accuracy and precision in bullet points:
- The main difference between accuracy and precision is that accuracy refers to how close a measured value is to the true or accepted value, while precision refers to how close the measured values are to each other.
- Accuracy is determined by the systematic errors or biases in a measurement, while precision is determined by the random errors or uncertainties in a measurement.
- Accuracy can be improved by calibrating instruments, reducing systematic errors, and using more accurate measurement techniques, while precision can be improved by increasing the number of measurements, reducing random errors, and using more precise measurement techniques.
- Accuracy is typically expressed as a percentage or an error value, while precision is typically expressed as a standard deviation or a coefficient of variation.
- Accuracy is important when the results of a measurement are compared to a reference value or a standard, while precision is important when the results of multiple measurements are compared to each other.
- Accuracy can be affected by factors such as environmental conditions, operator error, and instrument drift, while precision can be affected by factors such as instrument resolution, sample heterogeneity, and data analysis techniques.
| Accuracy | Precision |
| Refers to how close a measured value is to the true value | Refers to how close measured values are to each other |
| Determined by systematic errors or biases | Determined by random errors or uncertainties |
| Can be improved by calibrating instruments, reducing systematic errors, and using more accurate measurement techniques | Can be improved by increasing the number of measurements, reducing random errors, and using more precise measurement techniques |
| Typically expressed as a percentage or an error value | Typically expressed as a standard deviation or a coefficient of variation |
| Important when results are compared to a reference value or a standard | Important when results of multiple measurements are compared to each other |
| Can be affected by environmental conditions, operator error, and instrument drift | Can be affected by instrument resolution, sample heterogeneity, and data analysis techniques |