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Introduction to Infographics

Common Misrepresentations

When creating your infographic, it’s important to consider the ways in which your audience places their trust in your hands! Thanks to the visual nature of infographics, your audience is far more likely to place added value to the data you’re depicting and representing than they may initially give to longer form content. With an essay or book, audiences may be more prepared to engage critically and mindfully with the content they have set time aside to consume. Since infographics are typically so informationally dense and meant to be understood quickly, your audience is trusting that what you have to say is accurate, important, and reliable. 

Of course, while infographics will always be constrained by some loss of information and nuance, it’s up to you to ensure that the information you choose to depict is represented as effectively and accurately as possible. Below is a list of common misrepresentations that can be found in infographics, so that you can be sure you avoid misleading your audience.  

Click each heading below for more detailed explanations and visual examples. 

  • Omission of Data 
    • Leaving out key pieces of your data in order to represent a particular outcome is one of the most common ways that infographics can create false impressions with their audiences. Sometimes this means people will hide their baselines (or the axis of a graph) to create false correlations between the visual differences and actual values presented in their graphics. By failing to disclose the subjects of a study, the questions they were asked, how your data was collected, or by omitting conflicting data, audiences may be led to certain conclusions without knowing that key contexts have been entirely hidden from them. 
  • Biased Labelling 

    • Labelling your graphs, charts, or other visuals with vague or biased terms may confuse or inappropriately prime your audience to make certain assumptions or interpretations about the data you present. By using terms or phrases that have strong connotations that extend beyond their literal meanings, your audience may be more likely to interpret data in a way that can reaffirm stereotypical or otherwise inaccurate ideas. Terms like “freedom”, “justice”, “criminal”, “abnormal”, “important”, “good” or “evil” are just a few common terms that can be considered vague and loaded concepts. These terms will mean different things to different people, and may evoke certain other concepts which need proper definition before being associated with narrow datasets.  

  • Manipulating Y-Axis 

    • Similar to omitting data, some data visualizations may misrepresent the significance of a graph’s values. If one axis on a graph only depicts the most recent three years in which a decline in profit were observed, your audience may only perceive a sharp decline across a small section of time. With a wider, more complete baseline of 10 or 20 years, that sharp decline may seem miniscule compared to overall profits. By manipulating these axes, an infographic may be able to obscure more significant trends and concepts. This is usually done to make one group look better/bigger/more important than another, and often results in what’s called a truncated graph. Truncated graphs hide certain datapoints or overblow each datapoint’s value, so as to make small differences appear much bigger, or vice versa. 

  • Subverting Conventions 

    • Thankfully, there are conventions that have been developed to help make data visualization clear and understandable to wide audiences, allowing us to make generally safe assumptions about what a graphic is attempting to communicate. Unfortunately, when these conventions are subverted, it can be very misleading. For instance, if you showed a graph where a decline in profits were depicted in green, while increases were depicted in red, you may confuse your audience. Alternatively, if you were making a graphic on a map of population density, standard conventions would dictate that darker coloured sections of the map indicate higher densities, while lighter sections imply a lower density. If this expectation were subverted, audiences may, especially at a glance, misread your visualizations and draw entirely wrong conclusions about your data. 

  • Overcomplication 

    • Having overly complicated datasets, graphs, tables, or other visualizations is another common way that audiences can be misled, intentionally or not! By presenting too many variables, not clearly defining the context of the data, who or what was studied to achieve the data, or how the data fits into the broader message of an infographic, audiences will be unable to properly comprehend your content. Keeping your visualizations simple, direct, and relevant will help you get your point across. 

  • Pie Charts vs Tables 

    • Choosing the right graph to represent your data is one of the most important steps in creating an effective and reliable infographic. For instance, Pie Charts are pretty common in data visualization, but actually suffer from a few distinct flaws that make them unreliable. Humans aren’t very good at reading quantities at varying angles, and because of this, small percentages can be difficult to show. If you’re working with very simple, broad categories and you’re able to clearly label each section, Pie Charts may be useful, but when it comes to providing accurate data, some charts compromise more detail than others!  

It’s also important to keep in mind that other infographic creators may intentionally manipulate or misrepresent the data they discuss. Being able to spot these misrepresentations in your own work is just as important as identifying them in other peoples’ work. Check out this article from Medium on how data can be manipulated to suit biased narratives to learn more. 

Heads Up!: Click here to see Venngage’s Infographic on Misleading Data that summarizes many of the points made in the list above!