This graph was one that I found interesting when looking into the world and growth of esports. At first glance I thought that this graph was a decent representation of the number of fans viewing esports events over time. It represents the number of total viewers, the increase in viewership from year to year and divides between consistent viewers (called esports enthusiasts) and occasional viewers. This graph was released in September of 2020 by the Global Esports Market Report so was estimating for the end of the 2020 year. After listening to the podcast, I went back to this graph and was able to notice some of the deceptive parts of it. First off, the growth goes year by year from 2018-2020, and then jumps from 2020 to 2023 with no intermediary years. This makes the growth look exponential but in reality the growth seems to be slowing year by year. Additionally, at the very bottom of the graph in small print there is a disclaimer stating: “Due to rounding, Esports Enthusiasts and Occasional Viewers do not add up to the total audience in 2020”. This sentence really made me question the graphs portrayal of the data because if the estimate for 2020 was rounded, it is possible that other years were also rounded. Additionally, what does “rounded” mean? Does it mean rounding up by one-hundred or by one-million? This really made me question how sound the data of the chart was.
I found the reading to be very helpful in terms of reading graphs for both this class and others. Most of my other classes are in the sciences and we often look at graphics and charts in the same way, but consider the error to more be coming from the size of the dataset or technique. I had not really considered the error possible from people’s answers to polls or from the people crafting the graphics before. Looking at this made me evaluate how I make graphs and the way that I represent data in many of my biology classes. The reading offered an interesting way to look at not only charts in the humanities, but also a basis of understanding that can be transferred to other field of study.
You bring up a really important point in that even if a graph looks and seems accurate, it could not be. Reading the text closely and examining the numbers makes you realize that the data is rounded, such as in to your example, or deceptive or just plain wrong.
I think that it was a really good idea to read the fine print because it includes information that prevents the reader from misunderstanding the information. However, it begs the question how many readers actually look at that type of thing?
Cool graphic! The fine print is a huge thing to look at when analyzing anything like ads on TV, too, because it could say a cool bowling ball is $9.99, but shipping and handling could be $50, and you do not see this until after you impulse buy the ball lol!