CityReads│How to Spot Chart Lies?

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How to Spot Chart Lies?

Seven tricks help you spot the chart lies.

Jones, G.E. 2018. How To Lie With Charts, 4^th edition. LaPuerta Books and Media.

Sources: http://nautil.us/issue/19/illusions/five-ways-to-lie-with-charts

https://flowingdata.com/2017/02/09/how-to-spot-visualization-lies/

Chart's purpose is usually to help you properly interpret data. But sometimes, it does just the opposite. In the right (or wrong) hands, bar graphs and pie charts can become powerful agents of deception, tricking you into inferring trends that don't exist, mistaking less for more, and missing alarming facts. The best measure of a chart's honesty is the amount of time it takes to interpret it, says Massachusetts Institute of Technology perceptual scientist RuthRosenholtz: “A bad chart requires more cognitive processes and more reasoning about what you've seen.”

Based on How To Lie With Charts by Jones G.E., Five Ways to Lie with Charts by Becca Cudmore, and How to Spot Visualization Lies by Nathan Yau, today's post puts together seven tricks that help you spot the chart lies.

Puzzling Perspective

Source:http://www.mrexcel.com/tip142.shtml

Both of these pie charts show “labor” taking up 30 percent of some total. But you probably noticed that the chart on the right makes the labor slice look a lot bigger by positioning it in the foreground, which gives it a thick 3D edge and more than double the number of dark blue pixels than when it's in the background.

Human vision isn't very good at interpreting the third dimension, says Rosenholtz. When confronted with a 3D chart, we assume that more color indicates a greater amount. So when more pixels are used to represent one slice of a pie chart, the slice appears more significant, Rosenholtz says. That's why we can assign agreater value to foreground slices in 3D pie charts.

When you see a three-dimensional chart that is three dimensions for no good reason,question the data, the chart, the maker, and everything based on the chart.

Swindling Shapes

source: http://www.mrexcel.com/tip142.shtml的数据

A classic way to lie with a chart is to introduce irrelevant information. In the chart on the right, the only relevant property is cone height. But, while the cone volume is irrelevant, it is also very difficult to ignore, encouraging usto assign a greater value to the larger part of the cone.

In both charts, administrative costs take almost a third of each dollar. While this matches reasonably with the left chart, the right chart seems to shrink administrative costs to something much less than a third. “Anytime you ask anyone to judge just height and ignore the other measurements,” says Rosenholtz, “it’s going to take extra cognitive load to disregard these othercues.”

Trendsetters Are Tricksters

Source: http://tylervigen.com/

When two or more lines appear together in a chart, and they look similar to each other, we have the tendency to assume they are related. The red line in this chart represents suicide rates while the green line represents spending on science and technology—two completely independent sets of data. But on first glance, we tend to ask ourselves whether there could, in fact, be a causal correlation (if you can think of one, tell us in the comments section below).

We like trends because they tell a story that make data more meaningful, Rosenholtz says—that's why we're always on the lookout for connections, even when they don't exist.

Hiding in Plain Sight

Source: Data from Jones, G.E. How To Lie With Charts BookSurge Publishing, Charleston,SC (2006).

We're pretty good at noticing trends. But what if there's one that someone doesn't want us to see? The left chart clearly shows that marketing costs have tripled over three years. This same fact is there in the right chart, but it's hidden among a host of other data, softening the impact of the sharp incline in marketing costs, and making that incline nearly impossible to quantify.

“Comparing the change in height between data sets while they also move up and down is not a natural visual task for us,” says Rosenholtz. “It's not clear to me whether I'm supposed to be looking at the overall height or the width or what. Any kind of comparison like that is more cognitive and less effortlessly visual.”

Manipulating axis

Bar charts use length as their visual cue, so when someone makes the length shorter using the same data by truncating the value axis, the chart dramatizes differences. Someone wants to show a bigger change than is actually there.

Source: Data from Jones, G.E. How To Lie With Charts BookSurge Publishing, Charleston,SC (2006).

At first glance these two charts seem to depict two different data sets. But home in and you'll see that the only difference is scale.

This trick works because it's difficult for us to examine a chart's scale and data at the same time, says Rosenholtz. Instead, we often get the gist of the curve first, then (if we decide we need to) look at the scale. By that point, though, our first impression has already been made.

By using dual axes, the magnitude can shrink or expand for each metric. This is typically done to imply correlation and causation. 

The spurious correlations between divorce rate in Maine and the per capita consumption of margarine by Tyler Vigen is a great example.

Area sized by single dimension

If area is the visual encoding, then one has to size by area. When someone linearly sizes an area-based encoding, like a square or a circle, they might be sniffing for dramatics. Sometimes, it's an honest mistake. So be wary.

The graphic shows American government subsidies for different energy sources. It argues that there is a skew in funding for fossil fuels. Notice how much bigger the right bubble is compared to the rest; however, it should only be a little over four times bigger than the second from the right, and about 30 times bigger than that itsy bitsy, tiny bubble on the left. It's clearly wrong.

Here's the revised version. Bubbles are correctly sized by area (proportionate to the square root of the radius).

Limited scope

It's easy to cherry pick dates and time frames to fit a specific narrative. So consider history, what usually happens, and proper baselines to compare against. Interesting things can show up when you look at the big picture.

Climate change is a case in point. it’s more useful to look at trends over significant spans of time than isolated events. And, when you do look at a trend, it’s useful to have a proper baseline to compare against.

Asa rule of thumb, scrutinize charts that shock or seem more dramatic than you thought.

Achart doesn’t make something true. Data doesn’t make something true. It bends. It shows many things. So keep your eyes open.

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