Accuracy vs Precision
Within scientific research, collecting meaningful data is the main goal. The accuracy, precision and resolution of your measurements and final data set are all critical to the success of your research and the impact of your findings.
These three characteristics can be easily confused and misunderstood. Defining accuracy, precision and resolution and understanding the difference guides better data interpretation and decision-making.
Accuracy and Precision
Accuracy refers to the proximity of a measurement to the true value. It requires calibration against a known standard. This is represented either as an absolute offset, a percentage of the measured value, or a combination of the two. High accuracy means the experiment produces results close to the accepted values, giving confidence that the findings are correct.
Precision refers to how repeated measurements vary compared to one another. It is more about the reliability of the instrument to take the same measurement multiple times. Typically, this is represented as an absolute value. High precision means the results are consistent when repeated, but they may or may not be close to the true value.
You can determine the accuracy of your system by taking the mean and standard deviation of several repeat measurements. One way to measure precision is to calculate the relative standard deviation as a percentage:
A target is a common analogy to distinguish between accuracy and precision. It provides an easy visualisation to understand the relationship between them:
- High accuracy and high precision means measurements are very close to the target value and have little variation between them.
- High accuracy and low precision results in measurements that are close to the target value with high variability between them.
- Low accuracy and high precision means measurements are not close to the target value but there is little variation between them.
In every measurement, the aim is to improve precision and accuracy simultaneously. Yet in some cases, improving one may compromise the other. Understanding both helps researchers identify errors, improve methods, and make more reliable conclusions.
Resolution is the smallest change in a value that can be perceived by a measurement. Typically, this is represented as an absolute value. The resolution of a measurement will limit the accuracy and precision. If the resolution of your equipment is low, you are unable to measure very small changes accurately or precisely. High resolution enables you to measure closer to the true value and allows for more consistent repeated measurements.
The target analogy can also be applied to resolution:
- High resolution means incremental changes are smaller. This is demonstrated by more rings on the target.
- Low resolution means incremental changes are bigger. This is demonstrated by fewer rings on the target.
Having high levels of accuracy, precision, and resolution are all essential for generating meaningful data. Employing instruments with high resolution, calibrating systems for better accuracy, and identifying systematic errors to improve precision can dramatically enhance the reliability of your results. Failing to distinguish between and understand the role of each characteristic can lead to errors, misinterpretations, and ultimately, less impactful research.
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