Precision and accuracy are two important metrics to take into consideration when taking measurements. Accuracy is how close a measurement is to the correct value for that measurement. The precision of a measurement system refers to how close the agreement is between repeated measurements.

What Is Accuracy?

Accuracy is how close or far a given set of measurements (observations or readings) are to their true value. Taking small readings lowers the error rate in calculations. 

The accuracy of a system is classified in the following ways:

1. Point Accuracy: An instrument’s accuracy can only be determined at a particular point in its scale. It has nothing to do with the tool’s general accuracy.

2. Accuracy as Percentage of Scale Range: The range of the uniform scale determines the instrument’s accuracy.

3. Accuracy as Percentage of True Value: By identifying the measured value in relation to its true value, one can determine the accuracy of the instruments. The instrument’s accuracy can be neglected up to ±0.5 per cent from the true value.

Precision and Its Classification

Precision refers to the closeness of two or more measurements. High precision indicates that the measurements are consistent or that the readings are repeated. On the other hand, low precision means that the measurements vary. However, highly precise measurements do not always yield an accurate result. 

The precision of a system is classified in the following ways:

1. Repeatability: The variation occurs when the conditions are identical. Repeated measurements are taken over a short time.

2. Reproducibility: The variation arises when the same measurement procedure is applied across different instruments over a longer period of time.

Examples of Accuracy and Precision

If you are precise, that doesn’t necessarily mean you are accurate. However, if you are consistently accurate, you are also precise.

If you want to find which dataset is more precise, find the range, which means the difference between the highest number and the lowest number. For example, we have given two data sets:

• Set A: 10.46, 10.54, 10.48, 10.49
• Set B: 20.27, 20.30, 20.32, 20.36

Subtract the lowest data point from the highest:

• Sample A: 10.54 – 10.46 = .08
• Sample B: 20.36 – 20.27 = .09

Sample A has the lowest range (.08), so it is the most precise.

Here are some examples:

• If a given substance has a weight measurement of 10kg and the real or known weight is 10 kg, then the measurement is accurate.
• When you measure the value of resistance using a digital multimeter, the value of resistance is 35 Ohms, but the multimeter shows 33 Ohms consistently 10 times. So, the multimeter is precise but not accurate.
• Temperature thermometers can be accurate if they read 750F outside, and the temperature actually feels like 750F. They can also be precise if they register the same temperature several days in a row.

Difference Between Accuracy and Precision

Accuracy reflects how close a measurement is to a known or accepted value, while precision reflects reproducible measurements, even if they are far from the accepted value. Measurements that are precise and accurate are repeatable and very close to true values. 

Accuracy can be determined by a single measurement, while several measurements are required to determine precision. Accuracy is expressed in relative and absolute error terms, while precision is expressed in absolute and relative deviation. 

Conclusion

Accuracy is the degree of closeness between a measurement and the measurement’s true value. Precision is the degree to which repeated measurements under the same conditions are unchanged. 

Accuracy and precision are the essential factors to regard while calculating. Tools are required for measurement, which provides scientists with a quantity. 

The difference between accuracy and precision is that accuracy tells how near a measurement is to an available or accepted value. In contrast, precision is used to recall how reproducible the measurements are when they are distant from the accepted value.