Why the measurement function is important and how it forms the basis of uncertainty analysis. Introducing the uncertainty analysis tree as a way to visualise uncertainties.

Looking at the differences between errors and uncertainty and how a sensitivity coefficient converts uncertainties in effects to uncertainties in the measurand.

What are effects? An example uncertainty analysis tree. How do we think about effects and what do we need to know about them to combine uncertainties?

Where does error correlation come from? Different correlation structures.