Introduction » Training Objectives
Topics introduced in this lesson include the general characteristics of measured data and the importance of homogeneity when assessing how well the measurement represents the phenomenon; i.e., its “representativeness.” Measurement errors and uncertainty play a central role in discussions of instrument performance. In this lesson, we make an effort to quell the use of the term “accuracy,” which is often misused. We focus instead on the National Institute of Science and Technology guidelines for evaluating and expressing uncertainty. The lesson also describes the types or components of uncertainty that arise from systematic and random effects.
Upon completion of this lesson, you’ll be able to:
- Define and explain the static performance characteristics of an instrument.
- Describe the process of static calibration.
- Identify the difference between static and dynamic performance characteristics.
- Describe how the principle of superposition relates to signal analysis.
- Describe the dynamic performance characteristics for a first-order system.
- Distinguish between a first and higher order measurement system and associated energy reservoirs.
- Describe representativity and homogeneity by identifying features within an application that could generate internal boundary layers and affect these characteristics.
- Distinguish between Type A and Type B uncertainty.
- Apply the Law of Propagation of Uncertainty to a system of multiple sensors.