Calibration Measurement Uncertainty| Uncertainty Budget Type A and Type B
What Is Measurement Uncertainty? Full Guide to Uncertainty Budget, Type A & Type B Uncertainty (With Examples)
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Why Measurement Uncertainty Matters
In any ISO/IEC 17025:2017 accredited calibration laboratory, proving the accuracy of test results is essential. But accuracy alone is not enough—every measurement carries some degree of doubt, known as measurement uncertainty.
Understanding uncertainty is vital for:
✔ Calibration laboratories
✔ Quality control teams
✔ Manufacturers
✔ Testing and inspection bodies
✔ Auditors and assessors
This article explains what uncertainty means, how to prepare an uncertainty budget, and the difference between Type A and Type B uncertainty, using simple examples.
What Is Measurement Uncertainty?
Measurement uncertainty refers to the range of values within which the true value of a measurement is expected to lie.
Simple Definition:
Measurement uncertainty is the amount of doubt associated with the result of a measurement.
Why does it exist?
Because all measurement systems have limitations such as:
- instrument resolution
- calibration error
- operator influence
- environment (temperature, humidity, vibration)
- repeatability and reproducibility
Example:
A calibrated digital scale shows 50.00 g.
After analyzing uncertainties, the lab reports:
50.00 g ± 0.10 g (95% confidence)
This means the true value lies between 49.90 g and 50.10 g.
What Is an Uncertainty Budget?
An Uncertainty Budget is a structured table listing all individual uncertainty components that influence a measurement.
It contains:
- Source of uncertainty
- Value of each uncertainty component
- Probability distribution (Normal, rectangular, etc.)
- Divisor
- Standard uncertainty
- Combined uncertainty
- Expanded uncertainty
Why is it important?
✔ Required by ISO/IEC 17025:2017
✔ Demonstrates measurement reliability
✔ Supports customer confidence
✔ Provides transparency for audits
Example: Simple Uncertainty Budget for a Digital Scale
| Source of Uncertainty | Type | Value | Distribution | Divisor | Standard Uncertainty |
|---|---|---|---|---|---|
| Repeatability | Type A | 0.05 g | Normal | 1 | 0.05 g |
| Calibration Certificate | Type B | 0.06 g | Normal | 2 | 0.03 g |
| Resolution | Type B | 0.02 g | Rectangular | √3 | 0.0115 g |
| Temperature Effect | Type B | 0.04 g | Rectangular | √3 | 0.0231 g |
Combined Uncertainty (UC):
UC=sqroot{(0.05)^2 + (0.03)^2 + (0.0115)^2 + (0.0231)^2}
UC=0.064g
Expanded Uncertainty (k=2 for 95% confidence):
U=0.064×2=0.128g
Reported Result:
50.00 g ± 0.13 g (k=2)
🅰️ What Is Type A Uncertainty?
Type A Uncertainty is evaluated using statistical methods such as:
- repeated measurements
- standard deviation
- mean and variance
Characteristics of Type A:
- Based on data
- Involves repeated trials
- Calculated through statistical analysis
Example of Type A Uncertainty
You weigh a 100 g mass 5 times:
| Trial | Reading (g) |
|---|---|
| 1 | 100.02 |
| 2 | 99.98 |
| 3 | 100.01 |
| 4 | 99.99 |
| 5 | 100.03 |
Standard deviation of these readings = 0.02 g
This 0.02 g is the Type A uncertainty.
🅱️ What Is Type B Uncertainty?
Type B Uncertainty is evaluated through non-statistical methods, such as:
- calibration certificates (from accredited labs)
- manufacturer specifications
- instrument resolution
- environmental stability
- reference standards
- previous experience and data
Characteristics of Type B:
- Based on expert judgment
- Does NOT require repeated measurements
- Often uses reference documents
Examples of Type B Uncertainty
| Source | Uncertainty |
|---|---|
| Calibration certificate of weight | ±0.05 g |
| Instrument resolution | ±0.02 g |
| Manufacturer accuracy specification | ±0.1% of reading |
| Temperature variation | ±0.04 g |
Type A vs Type B Uncertainty (Quick Comparison)
| Feature | Type A | Type B |
|---|---|---|
| Based on | Statistical data | Expert judgment or documents |
| Requires repeated measurements | Yes | No |
| Examples | Repeatability, reproducibility | Certificate value, resolution |
| Distribution | Usually normal | Normal, rectangular, triangular |
Both contribute to the combined measurement uncertainty.
Why Measurement Uncertainty Improves Calibration Quality
✔ Strengthens ISO 17025 compliance
✔ Supports decision-making like PASS/FAIL
✔ Improves customer confidence
✔ Helps maintain traceability to national/international standards
✔ Enhances the laboratory’s technical competence
Real-World Example: Uncertainty in Calibrating a Vernier Caliper
Sources of Uncertainty:
- Repeatability (Type A): 0.01 mm
- Calibration certificate of reference gauge block (Type B): 0.005 mm
- Resolution (Type B): 0.02 mm
- Temperature variation (Type B): 0.015 mm
Final reported uncertainty might be:
±0.03 mm (k=2)
This ensures the customer knows how reliable the measurement is.
Understanding uncertainty, building an uncertainty budget, and knowing the difference between Type A and Type B uncertainty are essential for any organization engaged in measurement, testing, or calibration.
This knowledge not only ensures ISO/IEC 17025:2017 compliance but also builds trust, accuracy, and reliability in all measurement results.