What is the difference between accuracy and precision?

The accuracy of a measurement is a qualitative indication of how closely the result of a measurement agrees with the true value of the parameter being measured.  Because the true value is always unknown, accuracy of a measurement is always an estimate. An accuracy statement by itself has no meaning other than as an indicator of quality. It has quantitative value only when accompanied by information about the uncertainty of the measuring system. The accuracy of a measuring instrument is a qualitative indication of the ability of a measuring instrument to give responses close to the true value of the parameter being measured.  Accuracy is a design specification and may be verified during calibration. The accuracy of most instrumentation, is dependent on the accuracy of the device, or method used for calibration. Over time the accuracy can change. To preserve the right accuracy, proper re-calibration of instruments or tools is needed within a certain time frame.

Precision is a property of a measuring system or instrument. Precision is a measure of the repeatability of a measuring system – how much agreement there is within a group of repeated measurements of the same quantity under the same conditions. It refers to the closeness of agreement between measurement results, and indicates how consistent a process is. The better the precision the lower the difference amongst the values showing that the results are highly repeatable. High precision is only achieved with high quality instruments and careful work. Precision is usually expressed in terms of the deviation of a set of results from the arithmetic mean of the set.

Precision and accuracy are unrelated to each other, meaning that you can be very precise but not accurate or vice versa. Precision is also used as a synonym for the resolution of the measurement e.g. a measurement that can distinguish the difference between, 0.01 and 0.02 is more precise (has a greater resolution) than one that can only tell the difference between 0.1 and 0.2 even though they may be equally accurate or inaccurate.

Repeatability and reproducibility are two aspects of precision. Describing the minimum variability of precision, repeatability identifies variations that occur when conditions are constant and the same operator uses the same instrument within a short period of time. In contrast, reproducibility describes the maximum variability of precision where variations occur over longer time periods with different instruments and different operators.


Accuracy vs Precision


What is the difference between repeatability and reproducibility?

Repeatability and reproducibility are two aspects of precision.

Describing the minimum variability of precision, repeatability identifies variations that occur when conditions are constant and the same operator uses the same instrument, in the same location within a short period of time. Repeatability is expressed in the following statistical quantities: mean, standard deviation or the standard deviation of the mean. The smaller the number, the higher the repeatability, and the higher the reliability of the results.

Reproducibility is an important part of estimating uncertainty in measurement. It describes the maximum variability of precision where variations occur over longer time periods with different instruments, different locations or different operators. It measures the ability to replicate the findings of others. In other words: a reproducibility condition of measurement is another repeatability test where one or more conditions of measurement have been changed, to evaluate the impact it has on the measurement results.

What is metrology traceability and how do accuracy and measurement uncertainty relate to the traceability chain?

Metrology traceability is a property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations, each contributing to the measurement uncertainty. In a much simpler understanding, traceability is the series of comparison with the instrument’s measurement results (measured value and uncertainty) to the value of a higher accuracy standard that is linked to a more accurate standard until it reaches International Standards or SI.

The metrological traceability chain is 'the sequence of measurement standards and calibrations that are used to relate a measurement result to a reference'. It links every reference standard used in an order from high to low accuracy values until it reaches the lowest chain. When one chain is cut along the way, traceability is also disconnected in that part. You can no longer trace it back to the top chain.

The traceability pyramid presents the hierarchy of every reference standard and the size (magnitude) of the provided uncertainty. The higher the location of the reference standard in the pyramid, the smaller the measurement uncertainty it can provide, therefore, the more accurate the standard. Note: traceability is referring to the “result of measurement”, not the instrument, the standard, or even the calibration performed.

Metrology Traceability

What is calibration and why is it important?

Calibration is the process of adjusting an instrument or measurement system to ensure that it provides accurate and reliable results. Calibration is typically done by comparing the readings of the instrument or system with known or traceable standards, and making adjustments as necessary to bring the readings into agreement with the standards. The purpose of calibration is to ensure that the instrument or system is performing within its specified accuracy and to minimize measurement uncertainties. Calibration is performed with the item being calibrated in its normal operating configuration – as the normal operator would use it.

The result of a calibration is a determination of the performance quality of the instrument with respect to the desired specifications. This may be in the form of a pass/fail decision, determining or assigning one or more values, or the determination of one or more corrections. Calibration is performed according to a specified documented calibration procedure, under a set of specified and controlled measurement conditions, and with a specified and controlled measurement system.

What is error of measurement and which types of error are there in measurement?

In metrology, error (or measurement error) is an estimate of the difference between the measured value and the probable true value of the object of the measurement. The error can never be known exactly; it is always an estima te. Error may be systematic and/or random.

A systematic error is the mean of a large number of measurements of the same value minus the (probable) true value of the measured parameter. Systematic error causes the average of the readings to be offset from the true value. Systematic error is a measure of magnitude and may be corrected. Systematic error is also called bias when it applies to a measuring instrument.

Systematic errors

  • Produce values consistently in the same direction, e.g. 50 g, 1% or 77 mm too long or too small.
  • Affect the accuracy. The accuracy of an instrument can be significantly improved when the systematic error is minimised or compensated.
  • Cannot be reduced by increasing repetitions (and averaging the several measurement results).
  • Calibration and adjustment can reduce systematic error.
  • Can be computed when the model is known.
  • Cannot be easily detected or modelled
  • Can be compensated (corrected) by software or hardware
  • Possible sources: biases in the measurement system, calibration issues, or improper experimental setup.

Random error is the result of a single measurement of a value, minus the mean of a large number of measurements of the same value.  Random error causes scatter in the results of a sequence of readings and, therefore, is a measure of dispersion.

Random errors:

  • Produce different values in random directions. The results are unpredictable and can't be replicated.
  • Mainly affect precision. The repeatability of an instrument can be significantly improved.
  • Can be reduced by averaging from several measurement repetitions.
  • Require statistics and probabilistic analyses.
  • Can be easily detected by using a (positioning) sensor.
  • Cannot be compensated (unless using real-time error compensation methods with a closed-loop control).
  • Possible sources: electronic noise, environmental conditions, or limitations of the measuring instrument.

 Accuracy vs Biased

What is measurement uncertainty and how does it relate to accuracy?

Measurement uncertainty is the non-negative parameter characterizing the dispersion of the quantity values being attributed to a measurand, based on the information used. In simpler terms, measurement uncertainty is the estimate of the range of values within which the true value of a measured quantity is likely to fall. Measurement uncertainty and accuracy are closely related but distinct concepts. Accuracy refers to how close a measured value is to the true value, while measurement uncertainty is an estimate of how much the measured value might differ from the true value due to factors such as random errors, systematic errors, and limitations of the measurement method or instrument. A measurement can be accurate but have a high uncertainty if the range of possible values is large. On the other hand, a measurement can be less accurate but have a lower uncertainty if the range of possible values is small.

Measurement uncertainty vs error

To determine measurement uncertainty, several factors must be considered.

  • Random errors, which are unpredictable fluctuations in the measurement process, can arise from various sources and are assessed using statistical methods.
  • Systematic errors, on the other hand, are consistent deviations from the true value and can be caused by biases, calibration issues, or experimental setup problems.
  • Calibration of the measuring instrument is essential to establish its accuracy and correct any systematic errors.
  • The resolution and sensitivity of the instrument also affect measurement uncertainty, with resolution referring to the smallest detectable change and sensitivity to the ability to respond to small changes.
  • Environmental conditions, such as temperature and electromagnetic interference, should be controlled or considered to minimize their impact.
  • Operator skill and bias play a role in measurement uncertainty, as skilled operators can reduce random errors and identify systematic errors.
  • Proper uncertainty analysis involves identifying and quantifying all significant sources of uncertainty, combining them using statistical methods, and expressing the overall uncertainty with a confidence level.

Guidelines such as the "Guide to the Expression of Uncertainty in Measurement" (GUM) provide a framework for estimating and reporting measurement uncertainty.

What are the key considerations in selecting the right measurement instrument?

When selecting the right measurement instrument for a specific task, several key considerations should be taken into account: 

  • Accuracy: The accuracy of the instrument is crucial, as it determines how closely the measurements will correspond to the true value. Consider the instrument's accuracy specifications and ensure they align with the required level of precision for your measurements.
  • Precision: Precision refers to the instrument's ability to provide consistent and repeatable measurements. It indicates the degree of variability between multiple measurements of the same quantity. Higher precision is desirable when precise and consistent results are needed.
  • Resolution: Resolution refers to the smallest increment or smallest change in the measured quantity that the instrument can detect. Choose an instrument with sufficient resolution to capture the desired level of detail in your measurements.
  • Measurement Range: Consider the range of values that the instrument can measure accurately. Ensure that the instrument's measurement range covers the full range of values you expect to encounter in your specific application.
  • Environmental Conditions: Assess the environmental conditions in which the instrument will be used. Consider factors such as temperature, humidity, pressure, and electromagnetic interference. Some instruments may be sensitive to certain environmental conditions, which could affect their performance.
  • Calibration Requirements: Determine the calibration needs of the instrument. Consider whether the instrument requires periodic calibration and whether it can be calibrated easily or requires professional calibration services. Calibration ensures the instrument's accuracy and should be factored into the overall measurement process.
  • Sample Size and Handling: Consider the size and nature of the samples or objects to be measured. Some instruments may have limitations in handling large or small samples, irregular shapes, or delicate materials. Choose an instrument that can accommodate the sample size and type you will be working with.
  • Sampling Rate and Speed: If you need to measure rapidly changing or dynamic phenomena, consider the instrument's sampling rate and speed. Instruments with higher sampling rates can capture fast changes accurately, while slower instruments may miss important details.
  • Ease of Use: Evaluate the usability and user-friendliness of the instrument. Consider factors such as the instrument's interface, controls, display, and overall ergonomics. An instrument that is intuitive and easy to operate can improve productivity and reduce the chances of user errors.
  • Cost: Consider the budgetary constraints for the instrument. Balance the desired performance and features with the available budget. It's important to invest in a reliable and accurate instrument, but also ensure it provides value for the specific measurement needs.
  • Availability of Support: Consider the availability of technical support, documentation, and training for the instrument. Ensure that the manufacturer or supplier offers adequate customer support and resources to assist with any issues or questions that may arise during the instrument's use.
  • Compatibility: Evaluate the instrument's compatibility with existing measurement systems or software that you may have. Consider whether it can integrate seamlessly with other equipment or data analysis tools that are part of your measurement workflow.
  • Portability and Mobility: Evaluate whether the instrument needs to be portable or mobile for your specific application. Some measurement tasks may require on-site or field measurements, and portability becomes crucial. Consider the size, weight, and power requirements of the instrument for easy transportation.
  • Future Expansion and Upgrades: Anticipate future needs and potential growth. Determine if the instrument allows for expansion or upgrades, such as additional modules, accessories, or software capabilities. This flexibility can be advantageous as your measurement requirements evolve over time.

What is a picometre and with what can it be compared to in size?

A picometre is a unit of length in the International System of Units (SI) . It is equal to one trillionth (1/1,000,000,000,000) of a metre or 10^(-12) metres. The symbol for the picometre is 'pm'. It is incredibly small and is typically used to measure the size of atoms, molecules, and their interactions.

To give an idea of its size:

  • The size of a hydrogen atom is approximately 53 picometres. The distance between two adjacent atoms in a solid material can range from a few picometres to a few angstroms (1 angstrom is equal to 100 picometres).
  • The wavelength of visible light ranges from about 400 to 700 nanometres, which corresponds to 400,000 to 700,000 picometres.
  • The size of some molecules, such as DNA, is typically measured in nanometres to tens of nanometres, which is equivalent to thousands of picometres.

SI submultiples of metre (m)