Noise, Deviation, Anomaly, & Mistake

Image credit: Zachary del Rosario

It’s an exciting thing in science when you find something you didn’t set out to discover! Over the summer research period, I set out to better understand how engineers reason about variability. In the process of writing grants and designing interview protocols to elicit engineers’ thoughts, I had to do a lot of thinking on how variability “ought” to be defined. To that end, I developed a two-axis framework to describe variability.

These two axes lead to four disjoint “flavors” of uncertainty: noise, deviation, anomaly, and mistake. I’ll be teaching these ideas in Data Science this fall, and am currently operationalizing this framework as an interview protocol.

If you’d like to learn more, take a look at this draft chapter from a book I’m writing on modeling under uncertainty.

Zachary del Rosario
Zachary del Rosario
Assistant Professor of Engineering and Applied Statistics

Empowering scientists and engineers to reason under uncertainty