{"id":2100,"date":"2025-02-27T07:02:54","date_gmt":"2025-02-27T07:02:54","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/02\/27\/the-dangers-of-deceptive-data-confusing-charts-and-misleading-headlines\/"},"modified":"2025-02-27T07:02:54","modified_gmt":"2025-02-27T07:02:54","slug":"the-dangers-of-deceptive-data-confusing-charts-and-misleading-headlines","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/02\/27\/the-dangers-of-deceptive-data-confusing-charts-and-misleading-headlines\/","title":{"rendered":"The Dangers of Deceptive Data\u2013Confusing Charts and Misleading Headlines"},"content":{"rendered":"

The Dangers of Deceptive Data\u2013Confusing Charts and Misleading Headlines
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\u201cYou don\u2019t have to be an expert to deceive someone, though you might need some expertise to reliably recognize when you are being deceived.\u201d<\/p>\n

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<\/em> to deceive you, and who have studied some of the best ways to do it.<\/p>\n

I often start this lecture with a bit of a quip, looking seriously at my students and asking two questions:<\/p>\n

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  1. \u201cIs it a good thing if someone is gaslighting you?\u201d<\/li>\n
  2. After the general murmur of confusion followed by agreement that gaslighting is indeed bad, I ask the second question: \u201cWhat\u2019s the best way to ensure no one ever gaslights you?\u201d<\/li>\n<\/ol>\n

    The students generally ponder that second question for a bit longer, before chuckling a bit and realizing the answer: It\u2019s to learn how people gaslight in the first place<\/em>. Not so you can take advantage of others, but so you can prevent others from taking advantage of you.<\/p>\n

    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.<\/p>\n

    In this article, I\u2019ve taken the key ideas from my data visualization course\u2019s unit on deception\u2013drawn from Alberto Cairo\u2019s excellent book How Charts<\/a> Lie<\/em>\u2013and 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.<\/p>\n

    Humans Cannot Interpret Area<\/h2>\n

    At least, not as well as we interpret other visual cues. Let\u2019s illustrate this with an example. Say we have an extremely simple numerical data set; it\u2019s 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:<\/p>\n

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    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\u2019t really defined by a length or width. One option is to double the radius:<\/p>\n

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    Hmm. The first circle has a radius of 100 pixels, and the second has a radius of 50 pixels\u2013so this is technically correct if we wanted to double the radius. However, because of the way that area is calculated (\u03c0r\u00b2), we\u2019ve 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:<\/p>\n

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

    The issue here is trying to use area as a visual marker in the first place. It\u2019s not necessarily wrong<\/em>, but it is confusing. We\u2019re increasing a one-dimensional value, but area is a two-dimensional quantity. To the human eye, it\u2019s always going to be difficult to interpret accurately, especially when compared with a more natural visual representation like bars.<\/p>\n

    Now, this may seem like it\u2019s not a huge deal\u2013but let\u2019s take a look at what happens when you extend this to an actual data set. Below, I\u2019ve 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.<\/p>\n

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    Which one makes it easier to see the differences? The legend helps in the second one, but if we\u2019re being honest, it\u2019s a lost cause. It is much easier to make precise comparisons with the bars, even in a setting where we have such limited data.<\/p>\n

    Remember that the point of a visualization is to clarify data\u2013to make hidden trends easier to see for the average person. To achieve this goal, it\u2019s best to use visual cues that simplify the process of making that distinction.<\/p>\n

    Beware Political Headlines (In Any Direction)<\/h2>\n

    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\u2013but for the last question, I give them a chart I myself generated accompanied by a single question:<\/p>\n

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    Question: There is one thing egregiously wrong with the chart above, an unforgivable error in Data Visualization<\/a>. What is it?<\/strong><\/p>\n

    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.<\/p>\n

    The most flawed trait (or lack thereof, rather) in the chart above is the missing title<\/em>. 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\u2019t much.<\/p>\n

    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\u2013and in social media blow-up cases, the only thing that gets shared widely. As a result, it should have a title to explain itself.<\/p>\n

    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\u2019s look at an example:<\/p>\n

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    It’s time to end Chain Migration: https:\/\/t.co\/kad5A8Slw7<\/a> pic.twitter.com\/735JzAZIUa<\/a><\/p>\n

    \u2014 The White House 45 Archived (@WhiteHouse45) December 18, 2017<\/a>\n<\/p><\/blockquote>\n