Covariance is a statistical measure of how much two random variables change together. It's used by data scientists and machine learning engineers to understand the relationship between variables, but most software engineers don't need to think about it unless they're building a Netflix-style recommendation engine or doing something with heavy statistics.

How to use it in a sentence

• I was trying to update our user recommendations model, but I got stuck on calculating the covariance matrix for the different movie genres - maybe I should have paid more attention in my college statistics class!

• The product manager keeps talking about covariance this and covariance that ever since they read that pop science article on AI. I wish they'd just let me get back to coding and stop trying to sound smart.

If you actually want to learn more...

• An Intuitive Guide to the Basics of CUPED - This blog post provides a good overview of how covariance is used in the CUPED (Controlled-experiment Using Pre-Experiment Data) method for reducing variance in A/B tests. It covers the key mathematical concepts and how to implement it in practice.

• Experiment Interpretation and Extrapolation - This article by Tom Cunningham dives into some of the nuances of interpreting experiments, including how to think about covariance between different metrics. It discusses topics like selection effects, subgroup analysis, and how to handle multiple outcomes.

Note: the Developer Dictionary is in Beta. Please direct feedback to skye@statsig.com.