Error analysis is a compass, and we need it to be accurate.

Error analysis — the attempt to analyze when, how, and why machine-learning models fail — is a crucial part of the development cycle: Researchers use it to suggest directions for future improvement, and practitioners make deployment decisions based on it. Since error analysis profoundly determines the direction of subsequent actions, we cannot afford it to be biased or incomplete.

But how are people doing error analysis today? …


From academic courses to online articles, discussions of visualization often abound with design guidelines: Don’t use pie charts! Don’t use rainbow color maps! Ensure axes include zero! When applied in a proper context, such guidelines help prevent misleading visualizations. However, such guidelines are not always well-known, and are themselves subject to debate among practitioners, in part because the “proper context” may not be obvious.

As a result, design guidelines can be challenging to apply, relying on people’s judgment and expertise. What guidelines does my design adhere to? Are there exceptions to the “rules”? For example, we are often told a…


In daily life, we often find ourselves trying to separate signal from noise. For example, does the monthly jobs report suggest a growth trend, or that the jobs rate is steady? In a pair of experiments, we found that hypothetical outcome plots (HOPs) — animated samples of possible outcomes — can help people to make this judgment with greater accuracy.

How do people use HOPs?

HOPs enable viewers to experience variation in outcomes over time, similar to the way we experience uncertain events in our daily lives. Research finds that “a 3 out of 5 chance of rain” is easier to interpret than “a 60%…


The more visual comparisons an analyst makes, the more likely they are to find spurious patterns — a version of the Multiple Comparisons Problem (MCP) well known in statistical hypothesis testing. We discuss recent research from Zgraggen, Zhao, Zeleznik & Kraska (CHI 2018) that investigates this problem through a careful study of how a group of students identify insights in data using a visualization tool. We describe why studying MCP is exciting in its implications for work at the intersection of visualization, human-computer interaction, and statistics. However, we also question several assumptions made in studying MCP as a visualization process…


We are excited to announce the official version 2 release of Vega-Lite, a high-level language for rapidly creating interactive visualizations.

Vega-Lite enables concise descriptions of visualizations as a set of encodings that map data fields to the properties of graphical marks. Vega-Lite uses a portable JSON format that compiles to full specifications in the larger Vega language. Vega-Lite includes support for data transformations such as aggregation, binning, filtering, and sorting, as well as visual transformations such as stacking and faceting into small multiples.

In addition to an expressive range of static visualizations, Vega-Lite 2.0 adds support for flexible combinations of…


What if Visualizations Asked Users to Predict the Data First?

Imagine a data journalist writing a story about home prices in Denver. The journalist is planning to add a visualization to help users understand the trend of home price in 2014, 2015 and 2016.

The visualization could just show you the data, like we encounter all the time. But what if instead the interface prompts you to draw what you think the median home price in Denver looks like first?

Even if you don’t have specific knowledge about median home prices in Denver in 2014, 2015 and 2016, you might have some general prior knowledge that you could use to…


A single chart is often not enough to understand data and to convey a story. So it’s not surprising that people use multiple charts in sequence to convey their findings and craft narratives. For example, the following New York Times interactive article describes a global climate change agreement through a series of visualizations about countries who ratified or rejected the agreement:

However, not all sequences are created equal! Effective presentations involve a logical progression, with related charts shown in an appropriate order. …


William Playfair was an early pioneer of information visualization. Here is one of his charts, a 1786 depiction of the national debt of England:

William Playfair, The Commercial and Political Atlas, 1786.

The version I’ve posted here is too small to make out many of the finer details. Yet, I’d argue that this chart still communicates a clear message, even without those details: the national debt was at the time getting larger, and this trend was accelerating. Small reversals, such as the debt decreasing from 1762–1775, are not sufficient to counteract the overall impression of a strong, increasing trend. …


In 1977, Jerry Ehman — an astronomer working with the SETI project to seek out alien life — came across an interesting radio signal, one needle in the haystack of all of the electromagnetic signals that SETI monitors. An incredibly strong radio signal, one that matches many of the parameters we’d expect to see if aliens were really trying to communicate with us. So impressed was he with this data, that he circled the signal in red ink and wrote “Wow!” in the margins; it’s been called the “Wow!” signal ever since.

As the Wow! signal illustrates, often when we…

UW Interactive Data Lab

Data visualization and interactive analysis research at the University of Washington. http://idl.cs.washington.edu/

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