Understanding Types of Uncertainty in Measurement

In every industry, from petrochemical refining to aerospace manufacturing, accurate measurements are essential for operational safety, efficiency, and quality assurance. However, no measurement is free from uncertainty (that is, the statistical degree of confidence in how close a measurement is to its true value). Whether assessing temperature fluctuations in a reactor or calibrating critical instruments, understanding the different types of uncertainty is crucial for making informed decisions.

This article explores the major types of uncertainty–Type A and Type B—and their implications, providing practical insights and tools for effective management.

Why Understanding Uncertainty Matters

Uncertainty in measurement is more than a technical detail; it can have significant operational consequences. For example, a chemical reactor operating above its safe temperature threshold may result in catastrophic failures, while operating too far below this threshold could compromise productivity.

Industries such as aerospace, data center maintenance, and food manufacturing also depend heavily on precise measurements to ensure compliance, safety, and efficiency. For instance, temperature spikes in a data center can lead to server failure that creates extensive disruptions, such as when a system update caused server temperatures to rise rapidly, leading to service outages.

Such malfunctions and accidents stemming from imprecise or faulty measurements can result in financial losses for organizations and create safety issues for personnel, consumers, and the general public. Properly calculating an accurate and precise degree of uncertainty can help avert these types of incidents.

Types of Uncertainty in Measurement

There are two categories of uncertainty in statistical analysis: Type A uncertainty and Type B uncertainty. Both are necessary for engineers, statisticians, and metrologists to accurately calculate the precision and accuracy of a measurement.

Type A Uncertainty

Type A uncertainties arise from statistical variability in repeated measurements. In other words, Type A uncertainties are those that can be observed and measured repeatedly. They are quantified through statistical methods, such as calculating the standard deviation of multiple readings.

Type A uncertainties can have a number of sources, including environmental factors like vibration or electrical noise, or limitations of the measurement device, such as its resolution. For example, in a petrochemical reactor, temperature readings might vary slightly due to turbulence in the measuring medium.

Managing and reducing Type A uncertainties relies on several strategies and techniques. Common steps include:

• Gathering reproducible and repeatable data: Conduct multiple measurements to establish a clear statistical trend of variability. This starting point can help determine which factors might require adjustment to reduce variability and, ultimately, uncertainty.
• Using more accurate measurement devices: Select high-quality tools that come with low uncertainty specifications from the manufacturer, such as the Fluke® 787B/789 Process Multimeter. This Fluke Process Multimeter logs precise current and voltage readings to identify and reduce variability.

Type B Uncertainty

Unlike Type A uncertainties, Type B uncertainties stem from sources that are not statistical in nature and cannot be observed through repeated measurements. In other words, Type B uncertainties are all of the uncertainties that you cannot classify as Type A.

Similar to Type A uncertainties, Type B uncertainties can have a number of sources, including calibration drift, improper instrument setup, or external influences like temperature and humidity. For example, a pressure gauge might show a consistent bias due to calibration drift.

Managing and reducing Type B uncertainties can be difficult because, unlike Type A uncertainties, they do not stem from repeated observations and data collection. However, some primary methods for eliminating or reducing Type B uncertainties include:

• Use traceable, calibrated devices: Validate the accuracy and precision of measurement devices by calibrating them against reference instruments. Calibrated devices should be traceable to the SI.
• Maintain proper calibration schedules: Regularly recalibrate tools using high-precision equipment to ensure optimal measurements. Calibration frequency depends on factors such as frequency of use and proper usage.
• Use standards, guidelines, or other documentation, such as the manufacturer's specifications or the device's calibration certificate, to incorporate known environmental uncertainties.

Minimize Uncertainty of Your Device Measurements

The significant impact of measurement uncertainty across industries is evident. To help reduce this uncertainty, controlling and minimizing environmental influences can make a substantial difference. Additionally, some of the most critical mitigating steps include regularly calibrating instruments and using high-precision devices.

Fluke® calibration solutions offer a wide variety of precise, traceable calibration standards to help ensure your measurement devices perform accurately, consistently, and reliably.

FAQs About Measurement Uncertainty

Q: What is the difference between random and systematic errors?

Random errors cause unpredictable variability, while systematic errors create a consistent bias.

Q: How often should you recalibrate your instruments?

Recalibration frequency depends on usage intensity and industry standards, typically ranging from quarterly to annually.