Data visualizations – misleading or deceptive?

Data, data and more data – that is roughly how one could describe the current trends in society. We have in this age of digitalization seen a general hyper-connectivity spread throughout the world especially by different social networking platforms. This trend has furthermore spawned a situation where machines, companies and whoever wants the available information, will know more about me and you, than we probably thought we knew about ourselves. Data can be used and applied in countless different ways, which poses the question for companies of how they and other actors can maximize their output using the data available from their extensive databases?
It is widely known, that for example Big Data can act as valuable tool for marketing professionals in so far that it helps create more targeted ads, thus increasing the potential for businesses selling their products. But data can also function as the foundation of the creation of visualizations or illustrations (graphs, charts, diagrams etc.). These visualizations could be everything from a graph describing the past five year’s turnover in the annual statement, to a graph illustrating the expected benefits by acquiring the company’s product. But as Beattie and John Jones (2002) notes even information provided corporate annual reports are subject to inaccurate information (Bettie & Johan Jones, 2002). We may therefore suggest that it is evident that data can be applied and used in many different ways – well even to mislead or decieve. In my own opionion on data, I believe that the increased usage should ideally be used to enrich, improve and simplify our everyday lives, but this probably an utopoian thought as this is certainly not always the case.

I will claim that as more and more data become available it will create the foundation for more misleading and also deceptive visualizations being deployed. Furthermore I will contend, that there is a clear distinction between misleading and deceptive visualizations.

Misleading or deceptive – the theory behind

Data visualizations have always been subject questions about their validity, but as Albert Cairo notes “Charts, graphs, maps and diagrams do not lie. People who design graphics do” (Cairo, 2015, p. 104). Cairo also points to a clear distinction between deceptive and misleading graphics. He argues, that deceptive visualizations must have an intent to deceive by “knowing the truth and hiding it, or conveying it in a way that distorts” (Cairo, 2015). But Cairo also argues that a visualization can be misleading, but contends the difference being that this not a conscious intervention by the designer, but can be the result of “naive mistakes while analyzing the data or representing the data” (Cairo, 2015, p. 104) – so the difference between misleading and deceptive data visualizations, according to Alberto Cairo, lies in the intent of the designer.

However, different understandings and interpretations does exists in academic research. Pandey et al. (2015) argues that deception does not necessarily require intent by the designer. The authors stipulate that deceptive visualizations can be the seen as reflection of a poor skill-level by the designer – for example, not knowing best practice in statistics (Pandey et al., 2015). Although this is a valid point I admit to finding myself leaning towards agreement with Cairo. Because as mentioned above, it is my view that the increasing amount of data is leading to even more misleading information and therefore also deceptive tactics being deployed. Just think about Donald Trump and his proposed “fake news” – during his short time in office we have experienced everything from manipulated inaugurational pictures to more recently a doctored video of a CNN reporter Jim Acosta, which was deployed in order to make his actions look more aggressive (Harwell, 2018). But let me try to give some evidence to, what I would argue, is the difference between misleading and deceptive data visualizations.

Can a visualization show intent?

One of the common usages of misleading data visualizations is for the designer to use or display too much information in the graph (Cairo, 2015). As you can see in the picture below it is very difficult to make sense of what is happening – there is so much data being presented that it is impossible to single out any data points.

To many data points makes it impossible to isolate or make sense of the visualization (Hogle, 2018)

So why would somebody illustrate their data this way? There can be many reasons for this but one of the more prominent is, that it is a great way to “bury” bad news (Hogle, 2018). This begs the question of how to perceive this graph – is it misleading or deceptive? As argued above this classification rests upon the intent of the designer, as we in this case are not aware of the intent it raises the difficulty of reaching an indisputable conclusion. However, I will argue, that exactly this graph has more of a misleading than deceiving nature. Because even though it could “bury” some bad news, it does still present all the available data. I would therefore contribute its misleading character to what Cairo states; naive mistakes by the designer.

Another common usage, which is not directly a visualization problem, but I will argue is very important aspect in relation to data visualizations, is to describe or label the data inaccurately. This means, that even though the visualization in itself is accurately portrayed the explanation attached to the visualization is wrong and inaccurate (Hogle, 2018). In the picture below is an example of this.

Map illustrating the county-by-county results of the 2016 US Presidential Election (Hogle, 2018)

The data shown is a visualization that accurately portrays the county-by-county results of the 2016 US Presidential Election. The picture has been proudly used by Trump, and you can understand why. The visualizations clearly shows a majority of red (counties that voted Trump) in favor of blue (counties that voted Clinton). The problem of the illustration however becomes evident when looking at how this visualization was deployed in favor of Trump. This next picture of a book cover titled “Citizens for Trump” using the same visualization implies why.

Book cover showing how the same image can be made deceptive by use of text (Hogle, 2018)

As you can see the picture is attached with the word “citizens”. However, this word does not accurately reflect the data in the original visualization, as citizens, can be argued, to imply a notion of number of votes instead of number of counties. Furthermore, the counties in the midland are far less populated but represents a larger area, thus more red in the visualization (Hogle, 2018). Based on this information I will argue, that this illustration is not only misleading but deceiving, because it seems to portray a clear intent from the designer to alter the meaning of the visualization.

Final thoughts

Although this blog has not explored ethical considerations I think it is important to note, that these questions of misleading and deceptive visualizations are, as Cairo notes, shrouded with ethical questions (Cairo, 2015). This is also supported in a study by Marco et al. (2000) who states that “The reporting of data should be done with honesty and integrity, and every effort should be made to report data in the scientifically most accurate method” (Marco et al., 2000). But this discussion is for another time.

I would like to end this blog by asking you – Do you think that there is difference between misleading and deceptive data visualizations, and if so how important do you believe this distinction is?   



Beattie, V. & Jones, M. (2002). The impact of graph slope on rate of change judgments in corporate reports. ABACUS, 38 (2), 177-199. Retrieved from:

Cairo, A. (2015). Graphics lies, misleading visuals: Reflections on the challenges and pitfalls of evidence-driven visual communication. In D. Bihanic (Ed.), New challenges for data design (pp. 103-116). Springer-Verlag, London. Retrieved from:

Harwell, D. (November 8, 2018). White House shares doctored video to support punishment of journalist Jim Acosta. The Washington Post. Retrieved from:

Hogle, P. (August 15, 2018). Misleading Data Visualizations Can Confuse, Deceive Learners. Learning Solutions. Retrieved from:

Marco, C. A., & Larkin, G. L. (2000). Research ethics: ethical issues of data reporting and the quest for authenticity. Academic Emergency Medicine, 7(6), 691-694. Retrieved from:

Pandey, A. V., Rall, K., Satterthwaite, M. L., Nov, O., & Bertini, E. (2015). How deceptive are deceptive visualizations? An empirical analysis of common distortion techniques. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1469-1478). ACM. Retrieved from:

4 thoughts on “Data visualizations – misleading or deceptive?

  1. Hey! You explained the problem about misleading and deceptive data really good in your text. I think that there is definitely a difference between misleading and deceptive visualizations. If the authors of receptive data knows the truth and hides it, or conveys it in a way that distorts, it is an intentional action. Doing mistakes without noticing them, without a conscious intervention by the author, can lead to the same results, but does not lie in the intent of the designer. The intended deceptive data are in my opinion more dangerous, because the author tries to hide the truth by any means. The designer wants to reach particular aims and tries everything to convince the audience. Thus, a rectification may be prevented and becomes really difficult.


  2. Nice blog. I think it is an interesting topic. In regards to your final discussion point – I think the difference between misleading and deceptive data is that deceptive data intends on not showing the truth, whereas misleading data can be due to errors in how the data is read by the user. I think that the difference is important, those who actively try to mislead should be held more responsible for their misleading data. Whereas, others who mislead through human error in reading may not be as responsible. Yet, it is still the responsibility of anyone who is portraying information that they do so in a way that communicates the truth. Even if someone did not intend on misleading, if they find that users are reading the information wrong, then they should make changes and try to show the information in a clearer way. More education on how to represent information should be given not only to those who make the information but also the users how read the information.


  3. I really enjoyed reading this, because I think it is a quite important difference, because I believe the makers of the visualization do not always intend to hide the truth. Sometimes they might just lack in knowledge or think they did it the right way. So that shows the difference between deceptive and misleading right away. Because when someone does not hide some parts of the data on purpose, but because of the way I explained before, it is misleading to me. But if data is hidden on purpose, to make the person the story is about look better for example, it is definitely deceptive to me. But I think misleading data can be prevented by checking and also letting it check by others, because ‘stupid’ mistakes can lead to very impactful outcomes.


  4. When I read the title of your blog I wondered what exactly the difference was between misleading and deceptive data visualizations. I found that out during your blog. I do think that there is a difference between misleading and deceptive data visualizations. It is all about the intention of the designer. Whether you do something on purpose incorrectly or whether you don’t know better? Ultimately, for the interpretation of the audience it does not matter what the intention of the designer was. Furthermore, I agree with you that when more and more data becomes available, more misleading and also deceptive visualizations are likely to arise. Whether it is published intentionally or not.


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