On April 15, 2021, my book Now You See It (2009) will become available in its second edition with the revised subtitle An Introduction to Visual Data Sensemaking. Now You See It: An Introduction to Visual Data Sensemaking This is more than a mere update. Essentially, this new edition combines the contents of the first edition with the contents of my book Signal: Understanding What Matters in a World of Noise. I wrote Signal in 2015 to complement Now You See It by covering more advanced data sensemaking techniques, including Statistical Process Control. And, in case you’re concerned that this ..read more

We’re in the midst of a worldwide COVID-19 pandemic. Our understanding of this novel pandemic and our efforts to combat it are determined in large part by the information that we consider. It’s critically important that information in news stories is presented clearly and accurately. Unfortunately, sources that we rely on for the news, including ordinarily reliable sources, sometimes present COVID-19 data in misleading ways. This is sometimes done by omitting relevant data. Even one of my favorite new sources, NPR, was recently guilty of this. The charts that were included in an NPR article ti ..read more

The principles and practices of data visualization do not vary from one domain to another. They are the same. Data visualization applied to business differs only from data visualization applied to education (or healthcare, or government, or various branches of science, or any other domain you can imagine) in that each domain has its own data that must be understood before it can be visualized effectively. How the data is visualized, however, does not vary from one domain to another. All domains pull from the same repository of visual representations and, to work effectively, follow the same de ..read more

As COVID-19 spreads its deadly effects around the world, many data analysts are struggling to track these effects in useful ways. Some attempts work better than others, however. Comparing these effects among various countries is particularly challenging. Some attempts that I’ve seen are confusing and difficult to read, even for statisticians. Here’s an example that was brought to my attention recently by a statistician who found it less than ideal: I believe that the objectives of displays like this can be achieved in simpler, more accessible ways. Before proposing an approach that works bett ..read more

Statistics are playing a major role during the COVID-19 pandemic. The ways that we collect, analyze, and report them, greatly influences the degree to which they inform a meaningful response. An article in the Investor’s Business Daily titled “Dow Jones Futures Jump As Virus Cases Slow; Why This Stock Market Rally Is More Dangerous Than The Coronavirus Market Crash” (April 6, 2020, by Ed Carson) brought this concern to mind when I read the following table of numbers and the accompanying commentary: U.S. coronavirus cases jumped 25,316 on Sunday [April 5th] to 336,673, with new cases declining ..read more

We love to put things in order. “Which college is best, second best, third best, etc., and how can I get my kid into one near the top of the list?” “I love God, Mom, America, and apple pie, in that order.” “Formal education consists of elementary school, middle school, high school, undergraduate school, and finally graduate school, if you’re lucky.” “Our best salesperson is John, second best is Mary, Sally is third, and poor Harold is at the bottom of the list.” We sometimes forget, however, that when we sequence things, even when that sequence is based on a quantitative measure (e.g., salespe ..read more

It galls me when people oversell data visualization. Data visualization combines technologies (visual representations of quantitative data) with specific skills (techniques for creating and interacting with those visual representations) to make sense of and communicate quantitative data. It does not replace the other technologies and skillsets that are also needed to derive value from quantitative data; it complements them. It contributes to solutions; it is not “the solution.” As an expert in data visualization, I’ve never oversold it. Data visualization is extremely useful and at times esse ..read more

I just finished reading the book about Artificial Intelligence (AI) that I’ve been craving for years: Artificial Intelligence: A Guide for Thinking Humans, by Melanie Mitchell. More than any other book on this hot but largely misunderstood topic, this book describes AI in clear and accessible terms. It cuts through the hype to present a sane assessment with no agenda apart from a desire to inform. Reading this book, you’ll likely discover that AI is quite different from what you imagined. Melanie Mitchell qualifies as a second-generation pioneer in the field of AI. Beginning in the mid-1980s ..read more

As data sensemakers, we spend a great deal of time examining quantitative relationships. Along with distribution, correlation, and time-series relationships, proportion is the other quantitative relationship that plays a significant role in data sensemaking. A proportion is just a relationship between two quantities. If we compare the number of friends that Sally and John each have, Sally’s 20 friends compared to John’s 10 friends is a proportion. It’s really that simple, but confusion often occurs when we communicate proportions. Much of the confusion probably stems from the fact that proport ..read more

I often write about topics that I myself have struggled to understand. If I’ve struggled, I assume that many others have struggled as well. Over the years, I’ve found several mathematical concepts confusing, not because I’m mathematically disinclined or disinterested, but because my formal training in mathematics was rather limited and, in some cases, poorly taught. My formal training consisted solely of basic arithmetic in elementary school, basic algebra in middle school, basic geometry in high school, and an introductory statistics course in undergraduate school. When I was in school, I did ..read more