Deceptive Graphics
Friend or Foe ?
As a data visualization expert it is important to understand data deception techniques to not just avoid mis-stepping but also to identify deception and ask the right questions. One of the biggest lessons I have learnt in the short span since I started learning visualization is that data can be used to derive any conclusion you are seeking, as long as you know how to present it. Some standard deception techniques include: omissions, use of particular color schemes, following a certain narrative path, low rate of audience's data literacy and citing reliable data sources and leveraging confirmation bias.
The above visualization was created as part of the deceptive graphics assignment for Data visualization & storytelling class in Winter 2024. It was created using two separate data sets from StatCanada. It targets the common citizens of Canada to turn them against immigration as part of an election strategy, relying on audience's low data literacy. I decided to use vibrant red and white, specifically reminding people of the Canadian flag colors and create an infographic to be shared on social media and also printed as flyers if needed. While all the visuals are factually correct, they are not related or interdependent. Placing them together on this graphic and adding a provocative question in the title leads to the audience making deductions and arriving at their own conclusions. The end goal was for the Canadians to conclude that immigration is bad for Canada and will result in loss of jobs for Canadians.
Recently, in Nov 2024, me and my friend were travelling to the National Business Schools Conference (NBSC32) in Manitoba. During the flight, we were chatting about my decision to switch from accounting to technology management, which is that I couldn't see myself working as an accountant for the rest of my life and I thought it was very boring. Given that we were already going to a business school leaders conference, we took it upon us to see if I was right. This lead us to designing the flyer to the right as a joke. :p
While the above flyer was made as a joke, it is a classic example of deceptive graphic, hence worth being discussed here. The visual claims to come from a study, making a big claim of being conducted at a national conference of business school leaders, but the data is not verifiable or publicly available and neither is the method of data collected. I have used our university's colors to give it more legitimacy.
During the 3 day conference, me and my friends created the sheet to our left, and added our opinion of accounting students we met during the conference. On our way back, as we were analyzing the data, we noticed a 50-50 mix of both. That would disprove our hypothesis. Therefore, we filtered out all the students who had a minor with Accounting major. That served our notion.
While we did this as a joke, these are some common deception practices that a good data visualization expert needs to steer clear of but also to be wary of and to ask all the right questions of the data set being used.