Elections in USA just concluded and we all followed the news where we were constantly bombarded with numbers. This course made me realise that numbers should not be taken at face value as it can be consciously misleading. For eg. this post by President Trump made me wonder how is he claiming his approval rating to b ~90% when most of the news channels are showing favourable approval ratings for the democratic party's candidate Joe Biden. After a closer look at the post, I realized that Trump's visualization refers to his approval ratings amongst Republican supporters. Motivated by this trickery, I looked up the dataset for approval ratings for Barack Obama and Donald Trump.
Left Chart: The chart uses Anchoring technique as described under Procedural rhetorical techniques in the paper^[1]. This technique directs a user's attention in a way that subsequently helps in conveying a message. More specifically, the visualization uses filtering technique described in this section of the paper. It shows the approval rating of the candidate amongst people affliated(likely to vote or registered for a particular party) to his party. For eg. when Donald Trump is selected, the left chart shows his approval ratings amongst people who are registered/likely to vote for Republicans.
. This chart filters the survey data based on people's party affliation. Notice that there's fine text at the top of the visualization indicating that this chart is using filtered data. This text has deliberately been made small so that user's attention doesn't go to to the text.

Right Chart:The right chart shows the overall approval ratings for the selected candidate. In this chart the data has been as it is and no filtering has been applied. Therefore when all survey responses are considered it can be seen that the candidate's approval rating is actually quite lower than what is projected in the first visualization.

References:
[1]: Hullman, J. and N. Diakopoulos. “Visualization Rhetoric: Framing Effects in Narrative Visualization.” IEEE Transactions on Visualization and Computer Graphics 17 (2011): 2231-2240.