Writing about probability in a way that people will understand

We see probabilities mentioned in the news, in weather forecasts, during sporting events, political arguments, business reports, elections, medical advice, and scientific findings. But probability is a tricky concept that not all (most?) people understand. Grace Huckins for The Open Notebook outlines useful ways to communicate the numbers more clearly — to increase the chances readers do understand.

On using concrete numbers over percentages:

Concrete numbers can also make statistics feel more personally relevant. A 0.5 percent risk of developing a particular kind of cancer may seem minuscule. But if a reader went to a high school with 1,000 students, they may find it more impactful to hear that five of their classmates, on average, will develop the disease. In a March 2021 story, American Public Media used concrete numbers rather than percentages to communicate race disparities in COVID deaths. They reported that 1 of every 390 Indigenous Americans had died of COVID.

Other tips include using visuals, relatable comparisons, and acknowledging uncertainty instead of speaking in absolutes.

Tags:

Visualization accessibility

Øystein Moseng for Highcharts provides a brief guide on making your visualizations for accessible. Guideline #4 on not relying completely on color to show the data:

Relying on color as the only means of communicating information is a failure of one of the basic WCAG success criteria. This is because many users may not be able to distinguish between the different colors used.

Data labels, symbols, annotations and tooltips are some common ways to convey additional information in visualizations without relying on color. In addition, alternate presentations – such as an accessible data table – can be helpful. It is still encouraged to use color as a supplement to these techniques, this can make visualizations easier to understand, and in many cases more accessible.

Tags:

Visualization accessibility

Øystein Moseng for Highcharts provides a brief guide on making your visualizations for accessible. Guideline #4 on not relying completely on color to show the data:

Relying on color as the only means of communicating information is a failure of one of the basic WCAG success criteria. This is because many users may not be able to distinguish between the different colors used.

Data labels, symbols, annotations and tooltips are some common ways to convey additional information in visualizations without relying on color. In addition, alternate presentations – such as an accessible data table – can be helpful. It is still encouraged to use color as a supplement to these techniques, this can make visualizations easier to understand, and in many cases more accessible.

Tags:

Varying colors of state guidance maps

Many states use color to represent levels of Covid-19 and/or county restrictions. The color scales states use vary across the country. For The New York Times, Caity Weaver details the usage and the challenges of picking meaningful scales.

Tags: , , ,

Which color scale to use for your charts

On a superficial level, color scale selection seems like a straightforward task. Pick a sequence of colors that looks like it goes from light to dark. Done. But right when you get into it, you might find the process isn’t so straightforward. Different color scales can represent different aspects of your data, and poor selection can lead to poor communication. So, Lisa Charlotte Rost for Datawrapper wrote a four-part practical guide to help you figure it out.

See also Rost’s equally useful guide on what colors to pick for your scales.

Tags: , ,

Large collection of optical illusions and visual phenomena

Sometimes our eyes play tricks on us. Even when we know what is actually happening, our visual system won’t let us see the reality. Michael Bach has an extensive collection of 141 optical illusions, along with explanations of what’s tripping up:

Optical illusion sounds derogative, as if exposing a malfunction of the visual system. Rather, I view these phenomena as highlighting particular good adaptations of our visual system to experience with standard viewing situations. These experiences are based on normal visual conditions, and thus under unusual contexts can lead to inappropriate interpretations of a visual scene (=”Bayesian interpretation of perception”).

Tags:

Election map design challenges

For NYT Opinion, Betsy Mason outlines the design challenges behind election maps. Do you show geography? Do you focus on scale? What colors should you use? For every choice, there’s always tradeoffs, which is why there are so many views.

Tags: , , ,

Colour Controversy is a game of perception and labeling

Colour Controversy is a simple game that shows you a shade and asks you what color it is. The fun part is that the shades are usually in between two colors, say blue and green, and you can only choose one. A running tally is kept so that you can see the “most controversial” colors.

Tags: , ,

What states are doing to make mail-in ballots clearer

Mail-in ballots can be rejected if they’re not filled out or mailed correctly. A small percentage of them always are. This year, when we’re talking millions of mail-in ballots, even a small percentage means a lot of tossed ballots. For NYT’s The Upshot, Larry Buchanan and Alicia Parlapiano show how some states modified the design of their ballots to reduce the rejections.

Tags: , , ,

Telling stories in visual, data-driven essays

For The Pudding, Ilia Blinderman rounds out his three-part series on creating visual, data-driven essays. This last part in on the fuzziest task of telling stories:

Storytelling, however, is much more abstract — it’s not merely a technical matter of creating an image of a map, or designing the right chart; rather, it refers to the broader universe of considerations that impact nearly every decision you make in the way you frame and present a project. The focus is much less on the technical “how,” like in the first two installments of these guides, but on the “why” of designing the narrative. It certainly doesn’t help that technical tools are inherently more concrete: they’re ways of solving specific problems (e.g., “how do I show the locations where people are concentrated on a map?” or “how do get this visual element to move through this specific path?”), while storytelling is much more of a nebulous concept. Thus, in this guide, I’ll be focusing on the relevant questions and considerations that we, at The Pudding, tend to consider when creating data-driven projects.

Tags: , ,