Accuracy and precision are two measures of observational error. They are often used interchangeably in normal life, but as terms of measurement, they are defined differently.
-
Accuracy: It is the degree of closeness between a measurement and its true value. In other words, accuracy measures how close results are to the true or known value. It is obtained by taking small readings, which reduces the error of the calculation. Accuracy has two definitions:
- More commonly, it is a description of only systematic errors, a measure of statistical bias of a given measure of central tendency; low accuracy causes a difference between a result and a true value.
- According to ISO 5725-1, the general term "accuracy" is used to describe the closeness of a measurement to the true value. When the term is applied to sets of measurements of the same measurand, it involves a component of random error and a component of systematic error. In this case, trueness is the closeness of the mean of a set of measurement results to the actual (true) value.
-
Precision: It is the degree to which repeated measurements under the same conditions show the same results. Precision is a description of random errors, a measure of statistical variability. It shows how close the measured values are to one another.
A good analogy for understanding accuracy and precision is to imagine a football player shooting at the goal. If the player shoots into the goal, he is said to be accurate. A football player who keeps striking the same goalpost is precise but not accurate. Therefore, a football player can be accurate without being precise if he hits the ball all over the place but still scores. A precise player will hit the ball to the same spot repeatedly, irrespective of whether he scores or not.
In summary, accuracy measures how close results are to the true value, while precision shows how close the measured values are to one another. Both accuracy and precision are critical aspects of producing high-level work.
