People are regularly exposed to sensing and prediction in their daily lives (“will it rain today?”, “how long until my bus shows up?”, “who will win the next election?”). Uncertainty is both inherent to these systems and usually poorly communicated—or not communicated at all. However, understanding uncertainty is necessary for people to make informed decisions from data: If my bus is predicted to arrive in 10 minutes, what is the chance the bus shows up early, in 5 minutes—and do I have time to get a coffee? In this talk, I describe several projects investigating how people deal with uncertainty in their everyday data and how we can build systems that convey uncertainty in a way they can understand; several of these projects make use of frequency framing or discrete outcome approaches to uncertainty visualization. I will also preview ongoing work in systematically characterizing the space of uncertainty visualization designs and some tools designed to make uncertainty visualization construction easier. As we push more prediction into people’s everyday lives, we must consider carefully how to communicate uncertainty in ways that people can actually use to make informed decisions.