My key takeaways from this reading are that there are general guidelines and protocols, but no hard or fast rules. All of these best practices exist on a spectrum.
For example, chart-junk can be a distraction and pull away from the importance or insights of the data. Sometimes this can be intentional or unintentional. However, there are upsides to a certain amount of embellishment or artistic flair added to a visualization. In some cases, this ‘chart junk’ can improve recall or memorability, maybe even keep a viewer’s attention for a longer period of time.
Things like Gestalt principles can be used for both positive or negative ends, depending on how and where they’re used. Data can be manipulated (or rather, the visualization of the data) to make a graphic more persuasive or highlight key points. However, these manipulations can also tread into the realm of falsifying or misrepresenting information.
While reading this, sometimes I wondered: if given all of these potential pitfalls, why not just represent numbers as they are in their rawest, numerical format? Of course, the answer is easy: because it’s more challenging to read, comprehend, and see the key findings of the numbers in such a format. So even the data itself exists on this spectrum. Too much design can convolute the meaning of the information, and too little design makes the data more difficult to understand to its fullest extent.
I’m inclined to take a somewhat nihilistic approach as a response to this reading: it’s all nonsense, nothing is clear/honest/true. Everyone has an agenda so why interpret any of it because there’s an ulterior motive behind all of it. Of course, that’s not the point of this reading or this course as a whole. It’s better to understand these guidelines and spectrums in order to be a more judicious designer when making these choices for how best to represent information accurately, honestly, and (occasionally) with integrity.