(upbeat music) - As someone doing data communications, you need to understand what works and doesn't work, why best practices are worth following, why design and chart decisions you make will have an impact on your audience, and more. Fortunately, there are academics researching these topics to help us know we're making good decisions based on science rather than just using our intuition. One thing to keep in mind is that the field of data visualization research is relatively young and there's nearly infinite ground to cover. There are so many overlapping areas of study and so many issues at hand, like visual perception, cognitive bias, and more, that what we don't know is like an ocean compared to the bucket of what we do know. But this is changing, with lots of really smart people studying this stuff right now. Now, what I want to do is I want to talk about data visualization research and just point out a couple of studies that I found to be particularly interesting and that I find myself pointing to over and over when I teach my workshops on data storytelling and visualization. One study I find fascinating came out of MIT, which studied peripheral vision and how it impacts how we process visuals like infographics, maps, and charts. The researchers took a bunch of different visuals like this subway map image, a, and then applied a software filter to simulate what you would see in your peripheral vision if you were looking at the center of the map, that's c. So the center remains in focus, but the edges completely fall apart. This means that when you look at the image, you have a hard time processing the rest of it and struggled to know where to look next. Whereas the more abstract and simplified map, b, performs much better, even in peripheral vision, labeled d. The key lessons from this study are all able to be summed up in the idea of doing less with your design. Simplify, remove visual distractions, abstract away details. All of this allows the viewer to see things in their peripheral vision and therefore, she knows to look there next when consuming a graphic. In other words, every piece of design advice that I and many others give is confirmed by this study. And this study simultaneously reminds me that what we're always trying to do in data visualization is trigger a pre-attentive response to allow our audiences to see patterns, insights, and outliers in our data before we're even aware of it. This study, while not the most referenced study in the field, is a great reminder to me of important best practices to follow. Now, the other study I wanted to mention is the Useful Junk study. This study is essentially reversed years of bias against design embellishments that Edward Tufte caused because of his rants against what he did called chart junk. This study was the first that definitively found that visual embellishments like this monster actually increased long-term memories of the data being communicated and did not damaged short-term accuracy, either. In other words, chart junk can be beneficial. One thing to remember is that research and best practices are constantly evolving. For instance, we've been told by various studies that coffee is good for us. Then another study will say it's bad. Then the next one says we don't know if it's good or bad. Competing studies may have conflicting results. That's because data visualization research is very nuanced and not every experiment tests the same thing. Further, experimental design issues, such as sample size, statistics power, inherent biases, and a million other factors can influence results and conclusions. So you should never take one study and declare it as the truth and change everything you do because of it. My tip to you is to look for recurring patterns in the research that reinforce similar best practices. That way you can be more confident in decisions that you make. Up next, I'll talk to Petra Isenberg, who is one of the authors you see again and again on visual research papers. She'll have a lot of interesting insights to share, so stay tuned for that conversation.