Communicating risk in the context of daily living

Wayne Oldford, a statistics professor at the University of Waterloo, explains risk in the context of daily life at the individual level, because “one in a million” is not especially intuitive:

A few years ago, I was the “go to guy” at the University of Waterloo, asked to speak to local media, whenever a lottery jackpot got stupendously large (and the news cycle got exceedingly slow). My purpose was to relate to their audience the size of the chance of winning in a way that was quick yet comprehensible, which I did with some success on local radio and television stations.

Inevitably, though, the next day I would hear back of listener disappointment – that some of the fun of purchasing a ticket had been removed. Joy came from anticipating winning the prize and my exposition killed that for many, by them having gained an appreciation of the chance of actually winning.

I felt a little bit bad about this. I wanted people to understand the probabilities but I didn’t want to be a kill joy.

Important reading if you’re trying to understand the odds of things these days.

My favorite explanation of risk in the day-to-day is still the one from David Spiegelhalter.

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✚ Navigating Through the Uncertainty and Messiness of Data – The Process 179

Welcome to issue #179 of The Process, the newsletter for FlowingData members that looks closer at how the charts get made. I’m Nathan Yau, and this week I’m thinking about the uncertainty and messiness of data and how we connect back to the real world.

Become a member for access to this — plus tutorials, courses, and guides.

✚ Wide View (The Process 085)

The data might exist on a single page or in a single file, but there's always more to it. Take a step outside for a better view. Read More

✚ Less Time With Methods, More Time With Questions and Context (The Process #61)

Data represents the real world, and visualization represents data. But sometimes data and the real world disagree with each other. Read More

Techniques for adding context to visualization

When it comes to meaningful visualization, context is everything. Richard Brath, at the 2018 Information+ Conference, looks back on historical visualization approaches and how they might be applied today to make data graphics easier to read and use.

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Four

Got a chuckle out of me:

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✚ Axis Labels, Better Than Defaults (The Process #38)

In this guide, I look maybe a little too closely at how to adjust axis labels for more readable charts. Read More

GitHub is meant to track code

Jen Luker noted, “As amazing as @github is, it is a tool designed to track code, not people. I’m sharing my annotated GitHub history to show you what it can’t tell you about a developer.”

Data as footprints? Footprints can tell you where someone went, but you have to evaluate surroundings to figure out what he or she did along the way. And there’s a lot that can happen between when the footprints set and when you find them.

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Finding context for the data

Context makes data useful. Without it, it’s easy to get lost in numbers that mean little, but finding the context of data isn’t especially straightforward. Catherine D’Ignazio explains why it’s so hard and what data journalists (or anyone trying to understand data) can do about it:

First of all, data are typically collected by institutions for internal purposes and they’re not intended to be used by others. As veteran data reporter Tim Henderson, quoting Drew Sullivan, said to the NICAR community, “Data exists to serve the bureaucracy, not the journalist”. The naming, structure and organisation of most datasets are done from the perspective of the institution, not from the perspective of a journalist looking for a story. For example, one semester my students spent several weeks trying to figure out the difference between the columns ‘PROD.WASTE(8.1_THRU_8.7)’ and ‘8.8_ONE-TIME_RELEASE’ in a dataset tracking the release of toxic chemicals into to the environment by certain corporations. This is not an uncommon occurrence!

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