In every industry, from petrochemical refining to aerospace manufacturing, accurate measurements form the backbone of operational safety, efficiency, and quality assurance. However, no measurement is free from uncertainty (i.e., the statistical degree of confidence in how close a measurement is to the “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, offering practical insights and tools to manage them effectively. Let’s dive in.
Why Understanding Uncertainty Matters
Uncertainty in measurement isn't just a technical detail — it can have life-altering 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 like 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 massive disruptions, like when a 2013 Microsoft firmware update raised server temperaturesso rapidly that Hotmail and Outlook were down for nearly 16 hours.
Clearly, these types malfunctions and accidents that can stem from imprecise or faulty measurements not only cost companies time and money, they can also create safety issues for staff, consumers, and the general public. Properly calculating an accurate, 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 in order for engineers, statisticians, metrologists, and more to calculate how accurate and precise a measurement really is.
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 over and over again. They’re quantified through statistical methods, such as calculating the standard deviation of multiple readings.
Type A uncertainties can have a number ofsources, including environmental factors like vibration or electrical noise, or limitations of the measurement device like 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 a number of strategies and techniques. Some common steps include:
- Gathering reproducible and repeatable data: Conduct multiple measurements to establish a clear statistical trend of variability. This starting point can help you determine which factors might need adjusting to reduce variability and, ultimately, uncertainty.
- Using more accurate measurement devices: Look for high-quality tools that come with low uncertainty specifications from the manufacturer, like the Fluke 787B/789 Process Multimeter, which 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, since, unlike Type A uncertainties, they don’t stem from repeated observations and data collection. However, some of the primary methods for eliminating or reducing Type B uncertainties include:
- Only 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 International System of Units (SI).
- Maintain proper calibration schedules: Regularly recalibrate tools using high-precision equipment in order to ensure the best measurements. How often you should calibrate a device depends on many factors like frequency of use, proper usage, and more.
The Relationship Between Error and Uncertainty
While many people conflate error and uncertainty, in statistics and in metrology, the two are distinct concepts. Understanding that distinction is essential to ensure the most accurate, reliable measurements.
- Error: The difference between a measured value and the true value; it can be systematic (i.e., an error that happens consistently) or random (i.e., an error that happens unpredictably)
- Uncertainty: The range of possible measurement values, within which the true value lies; it can account for both systematic and random errors.
An example of an error is a temperature sensor that shows the temperature outside as 20°C, while the true temperature is actually 19.5°C. The error here is 0.5°C.
Meanwhile, the manufacturer of that temperature sensor might list its uncertainty as ±0.5°C; if the true temperature outside is actually 20°C, then the sensor, when properly calibrated, should show a temperature between 19.5°C and 20.5°C. If the sensor is showing a temperature outside of that range when the true temperature is 20°C, then the degree of uncertainty is too high and the sensor is no longer reliable.
Correcting both errors and uncertainties involves recalibrating the sensor so that its measurements are more precise and accurate.
Methods to Assess Uncertainty
Properly calculating uncertainty is an involved process and requires specific statistical analysis. However, there are some quick tips you can use to help you get started:
Use Statistical Analysis (Type A)
To assess Type A uncertainty:
- Take multiple readings under identical conditions.
- Compute the standard deviation to understand measurement dispersion.
Rely on Expert Judgment (Type B)
Assessing Type B uncertainty often requires external data or expert insight.
- Evaluate equipment calibration history.
- Use standards, guidelines, or other documentation (like the manufacturer’s specifications or the device’s calibration certificate) to incorporate known environmental uncertainties.
Minimize Uncertainty of Your Device Measurements
By now, it’s clear how much of an impact measurement uncertainty can make in any industry. To help reduce that uncertainty, steps like controlling and minimizing environmental influences can make an important difference. But some of the most important mitigating steps you can take include regularly calibrating your instruments and using high-precision devices.
Fluke calibration offers a wide variety of precise, traceable calibration standards to help you ensure your measurement devices are performing accurately, consistently and reliably.
FAQs About Measurement Uncertainty
Q: What’s the difference between random and systematic errors?
Random errors cause unpredictable variability, while systematic errors create a consistent bias.
Q: How often should I recalibrate my instruments?
Recalibration frequency depends on usage intensity and industry standards, typically ranging from quarterly to annually.