The Dangers of Deceptive Data–Confusing Charts and Misleading Headlines

February 27, 2025

“You don’t have to be an expert to deceive someone, though you might need some expertise to reliably recognize when you are being deceived.”

When my co-instructor and I start our quarterly lesson on deceptive visualizations for the data visualization course we teach at the University of Washington, he emphasizes the point above to our students. With the advent of modern technology, developing pretty and convincing claims about data is easier than ever. Anyone can make something that seems passable, but contains oversights that render it inaccurate and even harmful. Furthermore, there are also malicious actors who actively want to deceive you, and who have studied some of the best ways to do it.

I often start this lecture with a bit of a quip, looking seriously at my students and asking two questions:

“Is it a good thing if someone is gaslighting you?”

After the general murmur of confusion followed by agreement that gaslighting is indeed bad, I ask the second question: “What’s the best way to ensure no one ever gaslights you?”

The students generally ponder that second question for a bit longer, before chuckling a bit and realizing the answer: It’s to learn how people gaslight in the first place. Not so you can take advantage of others, but so you can prevent others from taking advantage of you.

The same applies in the realm of misinformation and disinformation. People who want to mislead with data are empowered with a host of tools, from high-speed internet to social media to, most recently, generative AI and large language models. To protect yourself from being misled, you need to learn their tricks.

In this article, I’ve taken the key ideas from my data visualization course’s unit on deception–drawn from Alberto Cairo’s excellent book How Charts Lie–and broadened them into some general principles about deception and data. My hope is that you read it, internalize it, and take it with you to arm yourself against the onslaught of lies perpetuated by ill-intentioned people powered with data.

Humans Cannot Interpret Area

At least, not as well as we interpret other visual cues. Let’s illustrate this with an example. Say we have an extremely simple numerical data set; it’s one dimensional and consists of just two values: 50 and 100. One way to represent this visually is via the length of bars, as follows:

This is true to the underlying data. Length is a one-dimensional quantity, and we have doubled it in order to indicate a doubling of value. But what happens if we want to represent the same data with circles? Well, circles aren’t really defined by a length or width. One option is to double the radius:

Hmm. The first circle has a radius of 100 pixels, and the second has a radius of 50 pixels–so this is technically correct if we wanted to double the radius. However, because of the way that area is calculated (πr²), we’ve way more than doubled the area. So what if we tried just doing that, since it seems more visually accurate? Here is a revised version:

Now we have a different problem. The larger circle is mathematically twice the area of the smaller one, but it no longer looks that way. In other words, even though it is a visually accurate comparison of a doubled quantity, human eyes have difficulty perceiving it.

The issue here is trying to use area as a visual marker in the first place. It’s not necessarily wrong, but it is confusing. We’re increasing a one-dimensional value, but area is a two-dimensional quantity. To the human eye, it’s always going to be difficult to interpret accurately, especially when compared with a more natural visual representation like bars.

Now, this may seem like it’s not a huge deal–but let’s take a look at what happens when you extend this to an actual data set. Below, I’ve pasted two images of charts I made in Altair (a Python-based visualization package). Each chart shows the maximum temperature (in Celsius) during the first week of 2012 in Seattle, USA. The first one uses bar lengths to make the comparison, and the second uses circle areas.

Which one makes it easier to see the differences? The legend helps in the second one, but if we’re being honest, it’s a lost cause. It is much easier to make precise comparisons with the bars, even in a setting where we have such limited data.

Remember that the point of a visualization is to clarify data–to make hidden trends easier to see for the average person. To achieve this goal, it’s best to use visual cues that simplify the process of making that distinction.

Beware Political Headlines (In Any Direction)

There is a small trick question I sometimes ask my students on a homework assignment around the fourth week of class. The assignment mostly involves generating visualizations in Python–but for the last question, I give them a chart I myself generated accompanied by a single question:

Question: There is one thing egregiously wrong with the chart above, an unforgivable error in Data Visualization. What is it?

Most think it has something to do with the axes, marks, or some other visual aspect, often suggesting improvements like filling in the circles or making the axis labels more informative. Those are fine suggestions, but not the most pressing.

The most flawed trait (or lack thereof, rather) in the chart above is the missing title. A title is crucial to an effective data visualization. Without it, how are we supposed to know what this visualization is even about? As of now, we can only ascertain that it must vaguely have something to do with carbon dioxide levels across a span of years. That isn’t much.

Many folks, feeling this requirement is too stringent, argue that a visualization is often meant to be understood in context, as part of a larger article or press release or other accompanying piece of text. Unfortunately, this line of thinking is far too idealistic; in reality, a visualization must stand alone, because it will often be the only thing people look at–and in social media blow-up cases, the only thing that gets shared widely. As a result, it should have a title to explain itself.

Of course, the title of this very subsection tells you to be wary of such headlines. That is true. While they are necessary, they are a double-edged sword. Since visualization designers know viewers will pay attention to the title, ill-meaning ones can also use it to sway people in less-than-accurate directions. Let’s look at an example:

It’s time to end Chain Migration: https://t.co/kad5A8Slw7 pic.twitter.com/735JzAZIUa

— The White House 45 Archived (@WhiteHouse45) December 18, 2017

The above is a picture shared by the White House’s public Twitter account in 2017. The picture is also referenced by Alberto Cairo in his book, which emphasizes many of the points I will now make.

First things first. The word “chain migration,” referring to what is formally known as family-based migration (where an immigrant may sponsor family members to come to the United States), has been criticized by many who argue that it is needlessly aggressive and makes legal immigrants sound threatening for no reason.

Of course, politics is by its very nature divisive, and it is possible for any side to make a heated argument. The primary issue here is actually a data-related one–specifically, what the use of the word “chain” implies in the context of the chart shared with the tweet. “Chain” migration seems to indicate that people can immigrate one after the other, in a seemingly endless stream, uninhibited and unperturbed by the distance of family relations. The reality, of course, is that a single immigrant can mostly just sponsor immediate family members, and even that takes quite a bit of time. But when one reads the phrase “chain migration” and then immediately looks at a seemingly sensible chart depicting it, it is easy to believe that an individual can in fact spawn additional immigrants at a base-3 exponential growth rate.

That is the issue with any kind of political headline–it makes it far too easy to conceal dishonest, inaccurate workings with actual data processing, analysis, and visualization.

There is no data underlying the chart above. None. Zero. It is completely random, and that is not okay for a chart that is purposefully made to appear as if it is showing something meaningful and quantitative.

As a fun little rabbit hole to go down which highlights the dangers of political headlining within data, here is a link to FloorCharts, a Twitter account that posts the most absurd graphics shown on the U.S. Congress floor.

Don’t Use 3D. Please.

I’ll end this article on a slightly lighter topic–but still an important one. Under no circumstances–none at all–should you ever utilize a 3D chart. And if you’re in the shoes of the viewer–that is, if you’re looking at a 3D pie chart made by someone else–don’t trust it.

The reason for this is simple, and connects back to what I discussed with circles and rectangles: a third dimension severely distorts the actuality behind what are usually one-dimensional measures. Area was already hard to interpret–how well do you really think the human eye does with volume?

Here is a 3D pie chart I generated with random numbers:

Now, here is the exact same pie chart, but in two dimensions:

Notice how the blue is not quite as dominant as the 3D version seems to suggest, and that the red and orange are closer to one another in size than originally portrayed. I also removed the percentage labels intentionally (technically bad practice) in order to emphasize how even with the labels present in the first one, our eyes automatically pay more attention to the more drastic visual differences. If you’re reading this article with an analytical eye, perhaps you think it doesn’t make that much of a difference. But the fact is, you’ll often see such charts in the news or on social media, and a quick glance is all they’ll ever get.

It is important to ensure that the story told by that quick glance is a truthful one.

Final Thoughts

Data science is often touted as the perfect synthesis of Statistics, computing, and society, a way to obtain and share deep and meaningful insights about an information-heavy world. This is true–but as the capacity to widely share such insights expands, so must our general ability to interpret them accurately. It is my hope that in light of that, you have found this primer to be helpful.

Stay tuned for Part 2, in which I’ll talk about a few deceptive techniques a bit more involved in nature–including base proportions, (un)trustworthy statistical measures, and measures of correlation.

In the meantime, try not to get deceived.

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