Within the introductory chapter of Healy's Data Visualization for Social Science, the importance of data visualization is emphasized. Seeing data visually allows for us to see correlation and relationships between data sets that are not visible by looking at just numbers. However, the outcome of a particular graph can vary greatly based upon the choices made to represent the data. The chapter was very informative in terms of giving good and bad examples of design choices such as lines, shapes, and colors. It was very eye opening in showing examples of the same data being dramatically different in terms of slight representation variations. I was introduced to various problems such as aesthetics, bad data, and bad perception. I've learned that aesthetics is an important part to data visualization in order for people to better understand the data and allow them remember the design itself better. Too much unnecessary "junk" takes away from the data itself, but without some sort of originality, the chart becomes ordinary and easily forgotten. Bad data refers to the way that data can be misinterpreted based upon the amount of context shown while bad perception takes into consideration the effects of a stable baseline and aspect ratio on how a graph is read.
Furthermore, the chapter touches upon the issue of intentional misrepresentation. Data visualization is a great way for allowing people to better understand data, but also it can be used with ill intentions to purposefully mislead the audience to believe in false information. I truly wonder whether there is anything considered truly objective within the world of data visualization. So much of it is created by humans and humans are known to have preferences and even biases. Is there a way to prevent being so? If so, how?