{"id":42053,"date":"2021-01-26T10:34:39","date_gmt":"2021-01-26T15:34:39","guid":{"rendered":"https:\/\/venngage.com\/blog\/?p=42053"},"modified":"2026-01-09T05:09:36","modified_gmt":"2026-01-09T10:09:36","slug":"bad-infographics","status":"publish","type":"post","link":"https:\/\/venngage.com\/blog\/bad-infographics\/","title":{"rendered":"Bad Infographics: The Worst Infographics from last 5 Year (+ Lessons from it)"},"content":{"rendered":"
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\"Bad<\/figure><\/div>\n\n\n

There\u2019s a long history of people creating and using infographics<\/span><\/a>, or visuals that communicate complex information. <\/span>They are meant to make information more accessible. Yet we see examples of poorly designed infographics<\/a>. It could be charts without proper context, designs that put style over substance, or visuals that simply don\u2019t connect with their audience.<\/p>\n\n\n\n

In this roundup, we\u2019re taking a closer look at some of the worst infographics shared in recent years. More importantly, we\u2019ll break down why<\/em> they failed and what you can learn from them, so you can avoid the same mistakes and create visuals that actually engage and inform.<\/p>\n\n\n\n

Lesson #1: Respect your audience<\/strong><\/h2>\n\n\n\n

An X user posted a video comparing rent prices to household income from 1990-2023. The viral falsely claimed both were “inflation-adjusted” when in reality only the income data was adjusted for inflation while rent data was not. A lot of people called him out in the comments, claiming his post to be ‘untrustworthy.’<\/p>\n\n\n

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\"Screenshot<\/a><\/figure><\/div>\n\n\n

Source:<\/strong> X<\/a> <\/p>\n\n\n\n

Sometimes the people who are creating infographics<\/a>, like statisticians or experts in government offices, make assumptions about the people who will ultimately consume those infographics, for example about their current level of knowledge. <\/span><\/p>\n\n\n\n

Other times, people create infographics with little knowledge themselves of data or statistics, or of graphic or information design, in order to share engaging visuals on social media. Or they know how to create graphs<\/a>, and they only use them to re-enforce the story they want to tell, regardless of their audience\u2019s needs.<\/span><\/p>\n\n\n\n

In every case, whether they realize it or not, they are using infographics to communicate with an audience. Keeping this audience in mind is key to communicating whatever information is at hand in a way that improves that audience\u2019s complete understanding of the topic.<\/span><\/p>\n\n\n\n

Lesson #2: This means don\u2019t mislead them by making bad infographics<\/strong><\/h2>\n\n\n\n

This really is the worst of the worst. These graphs are not merely filled with errors, they are grossly distorted, likely to serve specific agendas.<\/span><\/p>\n\n\n

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\"Example<\/figure><\/div>\n\n
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\"Example<\/figure><\/div>\n\n\n

<\/span><\/p>\n\n\n\n

You can use data to tell a story<\/a>, but you still need to represent that data in a way that is honest. That\u2019s all there is to say about that.<\/span><\/p>\n\n\n\n

Related:<\/b> 5 Ways Writers Use Misleading Graphs to Manipulate You<\/span><\/a><\/p>\n\n\n\n

Lesson #3: Quality data is a non-negotiable<\/strong><\/h2>\n\n\n\n

There are many myths about infographics but perhaps the most harmful one is \u201cnumbers don\u2019t lie.\u201d The trouble is, numbers never stand alone. <\/span><\/p>\n\n\n\n

First, they have to come from somewhere, and that means people are involved. Before it can ever be presented, data must be collected, organized and analyzed. Lots of things can happen across the board, whether they are intentional or not, so it\u2019s important to make sure the data used in infographics meets certain standards.<\/p>\n\n\n\n

Here\u2019s a great example<\/span><\/a> of a misleading infographic based on faulty data collection. Apple wanted to show how data about people\u2019s use of their Maps app was changing during quarantine. The presentation of this data implies that people were walking less, but what was overlooked was the fact that people don\u2019t need Maps to take walks around their neighborhood.<\/span><\/p>\n\n\n

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\"Misleading<\/figure><\/div>\n\n\n

Quality data comes from not only a reputable source, but from the right source. This graphic shows federal data but the numbers chosen are not \u201caccurate reflections of the problem of human trafficking or in many cases the identification of human trafficking,\u201d Director of the School of Criminology and Criminal Justice at Northeastern University Amy Farrell told FactCheck.org<\/a>.<\/span><\/p>\n\n\n

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\"Infographic<\/figure><\/div>\n\n\n

It also should go without saying that if you are using a certain data set, you shouldn\u2019t say you are using a different data set entirely. This map caused unnecessary panic because it was based on flight data not health data, according to the BBC<\/a>.<\/span><\/p>\n\n\n

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\"An<\/figure><\/div>\n\n\n

Data should also be up to date, particularly if timely data is critical to the story being told. Both of the following<\/span> infographics<\/span><\/a> flagged by<\/span> Reuters<\/span><\/a> did not include current data and therefore misrepresented the impact of COVID-19.<\/span><\/p>\n\n\n

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\"Infographic<\/figure><\/div>\n\n
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\"Infographic<\/figure><\/div>\n\n\n

The effort to keep data up to date caused alarm in the following case noted by PolitiFact<\/a>. When counties in Wisconsin and Michigan released a batch of election results, it appeared as a sudden uptick in Democratic votes for Joe Biden which caused some to believe there was evidence of election fraud. (The lesson that follows this one describes what could\u2019ve made the difference here.)<\/span><\/p>\n\n\n

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\"Infographics<\/figure><\/div>\n\n\n

If you aren\u2019t particularly data-savvy, that\u2019s okay, just don\u2019t pretend to be. The infographic below<\/span> (also flagged by<\/span> PolitiFact)<\/span><\/a> was created based on calculations that were incorrect because the numbers were based on different benchmarks. <\/span><\/p>\n\n\n

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\"Infographic<\/figure><\/div>\n\n\n

Regina L. Nuzzo, Ph.D. from the American Statistical Association told<\/span> Reuters<\/span><\/a> that these numbers attempt to over simplify a much more complicated reality.<\/span><\/p>\n\n\n\n

Lesson #4: Provide plenty of context in your infographic<\/strong><\/h2>\n\n\n\n

The reality is that numbers have to be explained, and this includes putting them in proper context. In some of the examples above, supporting text and\/or visuals could have provided audiences with a better understanding of how to best interpret the data. Here are some more examples of missed opportunities to do so.<\/span><\/p>\n\n\n\n

This graph makes the argument that masks help \u201cflatten the curve\u201d (or lower the rate of growth of COVID-19 cases) by pointing out that countries with mask usage had lower growth rates than countries without mask usage. <\/span><\/p>\n\n\n

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\"Infographic<\/figure><\/div>\n\n\n

Whether we agree with the argument or not, <\/span>this graphic conflates correlation with causation<\/span><\/a>, which the following humorous reply points to.<\/span><\/p>\n\n\n

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\"Infographic<\/figure><\/div>\n\n\n

It overlooks other potential factors to growth rates, like high-quality public infrastructure and practice preparing for a Coronavirus pandemic with the first one in the early 2000s. <\/span><\/p>\n\n\n\n

Many COVID-19 charts like the one below show case counts, and the context that\u2019s needed for these is that confirmed cases, as <\/span>The Atlantic<\/span><\/a> notes<\/span>,<\/span><\/a> provide a very limited way of knowing how much the virus is spreading if for no other reason than testing is not something every single person is doing. This chart of confirmed COVID-19 cases across US states gave the false impression that just a handful of states had the vast majority of cases. <\/span><\/p>\n\n\n

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\"COVID-19<\/figure><\/div>\n\n\n

Alternatively, a scatterplot using the same data (like this one created by Popular Science<\/a>) but showing the relationship between confirmed cases and testing rates would clearly show that places with the highest infection rates also have the highest testing rates. If you don\u2019t know, this is what a scatterplot looks like. Stay tuned for more about types of graphs.<\/span><\/p>\n\n\n

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\"Scatterplot<\/a><\/figure><\/div>\n\n\n