Accuracy and Precision are two important concepts often used in scientific measurements and data
analysis. They describe the quality and reliability of measurements but are distinct in their meanings.
Accuracy
Definition: Accuracy refers to how close a measured value is to the true or accepted value. It
indicates the correctness of a measurement.
Example: If the true length of a rod is 100 cm and you measure it to be 99 cm, the measurement is
accurate because it is close to the true value.
Representation: Accuracy can be represented by the degree of agreement between the measured
values and the true value.
Precision
Definition: Precision refers to how close the measured values are to each other, regardless of
whether they are close to the true value. It indicates the repeatability or consistency of
measurements.
Example: If you measure the length of a rod multiple times and get values of 99 cm, 99.1 cm, and
98.9 cm, the measurements are precise because they are very close to each other.
Representation: Precision can be represented by the degree of spread or variability in the
measured values.
Key Differences
- Accuracy is about correctness.
- Precision is about consistency.
Visualization
Imagine a target with a bullseye representing the true value:
High Accuracy, High Precision: Measurements are both close to the true value and close to each
other, clustered around the bullseye.
High Accuracy, Low Precision: Measurements are close to the true value but not close to each
other, scattered around the bullseye.
Low Accuracy, High Precision: Measurements are not close to the true value but are close to each
other, clustered away from the bullseye.
Low Accuracy, Low Precision: Measurements are neither close to the true value nor close to each
other, scattered widely.
