In a world where everything has gone completely digital, the widespread use of making and distributing data visualizations in order to interpret statistics, information, and any form of data is not only making it very easy for the general public to get a better understanding of the more important numbers and information in their lives, but it also makes it easy for designers to create these charts with the intent of throwing off their audience and providing them with a visualization that tells a completely different story than what the data is truly telling.

This is a short presentation about data and information visualization, statistics, how misleading graphs are made and the ethics behind them.

About the Author Gabe Walerysiak

My name is Gabriel Walerysiak, and I am a graduate student at Quinnipiac University in Hamden, Connecticut. I graduated with my Bachelor's Degree in Film, Television and Media Arts, with a minor in Mathematics in June 2020. I am currently pursuing a Master of Sciene in Interactive Media and Communications and will graduate with my master's degree in May 2021. I recently interned at GlucoseZone this past summer, where I edited and helped film a bunch of promotional content for their social media pages. I have a hobby of capturing and editing video-game footage for a YouTube channel that I started in the Seventh grade, and that channel is the primary reason I chose to major in Film, TV, and Media Arts. I am also a passionate runner, and even though I am no longer on a team, I run to keep in shape because I know how important that is in today's world. I am looking forward to be more fluent with technologically enhanced creative programs such as the Adobe suite, productivity tools such as Microsoft Office, and any other creative tools I can get my hands on to further improve my work as a creator.


  1. Hi Gabe!

    Your discussion of data visualization was very informative and greatly explored the issues in that area of design. Hearing about the skewing of the Y-axis and X-axis was very eye-opening because when most people look at charts or graphs, they scan them very quickly. I think you could really expand your topic by showing famous or widely known examples of data graphics that are skewed yet accepted. You briefly mentioned that pie charts are the worst when it comes to misinterpreted information, could you also investigate some examples where pie charts are praised for their design? In your paper you could discuss the acceptance of certain data graphics from objective and subjective viewpoints. Since you also talked about the perception of colors, maybe you can write about how certain data graphs can also yield an emotional response that may contradict or compliment the information being shown.

    If you wanted to link back some of your info to more sources from the modules, week 6 has articles that discuss how data visuals feel incomplete or confusing because they do not provide enough clarification of what is being shown. You may be able to add this issue to the ones you have already mentioned in your presentation.

    Overall, I thought you had a strong presentation and I think there are many ways you can expand your topic.


  2. Gabe – I am so happy to see that you explored this topic in your presentation. The importance of showing the whole truth is pivotal especially in a time when many people will look at a graph or chart and assume that it is 100% true.

    I think that one area of improvement for this presentation would be to use images that pertain to what you are speaking about at that exact moment – having to listen to you speak about one thing and look at a photo that is telling a (slightly) different thing is a bit distracting. In your final essay I think you could easily solve this by simply putting a caption below each visual explaining what is wrong (or right) with it.

    I think one of your most interesting points had to do with using certain colors to elicit emotions from a viewer. I would love to see you go further into this point in your final essay. I also agree with Liana, I think it would be great for you to show specific examples of graphics being viewed from subjective and objective viewpoints.

    Overall you did an excellent job and I am really looking forward to your final!


  3. Hi Gabe,

    Excellent topic! Data visualization is a big personal passion and professional area of interest for me.

    First, when you’re indicating trends on a static image, I recommend using a line to indirect the specific tilt of the trend. This will help your viewer more easily draw the intended conclusions from some of your comparisons between good/bad graphs. Another important thing to consider is that Ignite presentations should not include text, so you may want to consider other ways to communicate some of the information included in your infographics.

    Next, don’t assume that your audience will be able to automatically take away what you want them to from the visuals. You’ll want to specifically point out the incorrect elements of graphs as they pass by. The combination of the text and visual and their attention split between the two can otherwise keep your punch from landing. It looks like the majority of your examples in the first 2/3 include the absence of 0 on the y axis; consider incorporating other aspects of misleading elements to ensure you have enough variety to explore.

    Good luck with your final!


  4. Dear Gabe,

    This is a very interesting topic and it is something I kind of learned about in high school. Graphs themselves have the innate ability to be manipulated to present the information in an almost untruthful way. This is not only true for data visualizations but also for any display of data. I am glad you incorporated how different graphs can and should be used for different types of data, for this alone will alter the display of the data. I think that color can be one of your main points when it comes to data visualization as there is a lot of evidence connecting color and emotions. It will also relate more to data visualization rather than just data graphs alone so it will help keep you on topic. I am also glad you incorporated the difference between causation and correlation. It is a basic statistics rule but is almost exploited in persuasive visualizations. I find it interesting that you talk about the ‘why’ data is manipulated as it will allow you to draw connections to practices in certain industries which will give you better examples of statistics that lie. The idea of the designer trying to explain the idea of the data as ‘best’ as they can be one of your main points as it is a root cause as to why data can be seen portrayed differently and possibly inaccurately.



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