In today’s fast paced technological world, data visualization is extremely important. People spend very little time reading and taking in information; they have very short attention spans. Accordingly, it is important to have data literacy skills in order to properly interpret and question information presented to you. Quick passes are often not enough to catch important information. Knowing some basic data literacy skills can help spot misleading data quickly. Our discussions and reading this week alluded to the biases that can be present in data. In addition to biases in the actual collection of the data, biases can be present in the data presentation, misleading the consumer into false conclusions about the data.
We analyzed a few examples of poor graphics in order to examine the core principals that they violated. The two examples I examined are below.
This first graphic violates the principal about scale. They mention that orange is food and drinks and that entertainment is pink. However, they do not provide a scale. It is impossible to tell where orange ends and pink begins, they just melt together. The user essentially gains no information from viewing this graph. Also, while the relative magnitudes of the days can be seen, a lack of a vertical scale also makes it impossible to tell how much was actually spent. It looks like Monday ~2* as much money was spent as Tuesday, however it could be $1 vs $2 or equally it could be $100 vs $200.
This graphic has major issues on all accounts. First off, besides the obvious typos, the numbers do not add up to 100%. There is a 10% segment that is not counted, which is misleading. Additionally, the block on the left is the same size as the two blocks on the right added together, however the block on the left is double the combined percentage of the two on the right. It should therefor be twice as tall. This violates the lie factor.
