Accuracy and precision are two terms commonly used in the context of measurements and data analysis. They refer to different aspects of the quality and reliability of the results obtained from a process or an experiment. Here’s the difference between accuracy and precision:
Accuracy refers to how close a measured or observed value is to the true or target value. It assesses the correctness of the measurement. In other words, accuracy tells us how well a measurement represents the actual value it is intended to measure. An accurate measurement means that the measured value is very close to the true value. If a measurement is accurate, it has minimal systematic error, which is the deviation between the measured value and the true value.
Example: If a scale displays 100 grams for a 100-gram weight, it is considered accurate.
Precision, on the other hand, deals with the consistency and reproducibility of measurements. It quantifies how close multiple measurements are to each other, regardless of their closeness to the true value. A precise measurement indicates that repeated measurements of the same quantity will yield very similar results. Precision assesses random errors, which are the variations between individual measurements.
Example: If a scale consistently displays 98 grams for a 100-gram weight in multiple trials, it is considered precise.
To summarize, accuracy is about how close a measurement is to the true value, while precision is about the consistency of multiple measurements. A measurement can be accurate but not precise if it is consistently off by the same amount from the true value. Conversely, a measurement can be precise but not accurate if it consistently provides similar results that are far from the true value. The ideal scenario is to have both accuracy and precision, where measurements are both close to the true value and consistent with each other.