"Required reading" says Arkansas Business


In a column that referenced John Oliver and Tyler Vigen (who we quote in our book), Arkansas Business editor Gwen Moritz argued that EVERYDATA "should be required reading in high school and for every journalist and journalism student in the universe."

Here are a few of our other favorite quotes:

"Using sentences and examples that even I can understand, Johnson and co-author Mike Gluck explain the way averages can be used and misused."

"They write about the problem with self-reported data... [and] warn us to consider whether important data might be missing."

"We can either be smart, skeptical consumers of data or suckers. Take your pick."

You can read  Gwen's full article here.

Was Jonas Really a $3 Billion Dollar Blizzard?

As the East Coast digs out from the Blizzard of 2016, a recent report from Moody's Analytics estimates the cost of the Blizzard to be between $2.5 and $3 billion dollars. When there are major weather events of this nature, we often see these types of estimates calculated and widely reported in the media.

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Tablet vs Paperback?

In our forthcoming book, Everydata, we very briefly address an interesting study from the University of Oregon that finds "People actually recall more information when they read a printed newspaper versus reading it online." Our purpose in raising the study was not to closely examine the underlying statistical methodology (though we might have something to say about the sample of 45 people) but to introduce the concept that how you receive your data can also effect how you interpret or retain it.

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Infographic of the Week: Dangerous Jobs

Source: Fast Company

In this featured infographic from Fast Company, a simple bivariate relationship between risk of death and higher pay is illustrated.  This phenomenon in economics is known as a compensating wage differential--the idea that people are paid more to compensate them for taking on the higher risk of their jobs.  Most people would prefer to get high pay for low risk--jobs illustrated in the upper left.   Similarly, unless you get incredible utility from risking your life, you would want to avoid jobs illustrated by dots on the lower right...high risk and low pay.

The full interactive graphic comes from Bloomberg and can be found here.

A Guide to Data Visualization

Source: UX Motel, @FlavienP
Source: UX Motel, @FlavienP

One of the most interesting parts of having an active twitter feed is the immediate feedback you get to posts based on retweets, quotes, and favorites.   I found this chart on twitter and retweeted it, and it has been one of my most popular tweets.  I thought it would be valuable to feature it on the blog.

This chart gives some simple guidance as to how to think about visually displaying your data based on what it is you'd like to highlight.   The chart breaks things into four useful categories of relationships:

Composition:  If you'd like to highlight how a key variable is changing over time, the bar charts and pie charts provide a way to highlight compositional changes.   Our friends at FlowingData.com provide this doozy of a pie chart for your entertainment pleasure.   But all kidding aside, a well-made pie chart can be a tremendously powerful way to illustrate composition and I find them to be intuitive even to non-statistically oriented types.

Distribution:   Illustrating distributions is about trying to show the full spread of the data.  I think most people think of distributions by hearkening back to the results from an exam in high school;   the teacher explains that 3 people in the class got an A, 15 people got a B, and 3 got a C, and 1 got an F--this is a distribution.   I suspect most people (if they think about distributions at all) are familiar with the bell curve.  Here is an interesting Forbes article, however, on research that suggests that in the work place, most value is created by a small percentage of hyper performers at the very top of the distribution.

Comparison:  How do the experiences of one group differ from the experiences of another?  In comparison charts, we simply want to draw out similarities or differences between the outcomes or experiences of different sets of people.   In this line chart from a recent article on income inequality in The Huffington Post, we see a comparison of real average after-tax income for different wage earners over time.

Relationships:  In the real world, we think all the time about how two things relate.  If I spend more time exercising, how much weight will I lose?  If I save an extra dollar today, how much more will I have for retirement in 20 years?  Charts designed to show the relationships between two variables abound--and can be some of the most misleading or informative depending on presentation and content.  More on this later--but for now, enjoy this article on the relationship between margarine and divorce rates.