Lifehack: Manipulate Data to Justify the unhealthy student lifestyle

When learning and analyzing data, it is crucial to recognize the human bias and error that can intentionally (or unintentionally) manipulate the viewer.

The objective of this project was to analyze a student’s data and figure out how it might help their life be more enjoyable, productive, and manageable. I chose to make the results of the data more convenient to the student, instead of vice a versa.

I gathered data from a peer tracking their stress levels, caffeine intake, and hours of sleep per night. I decided beforehand to make the argument that more caffeine and less sleep makes you less stressed, and designed the result with this argument in mind. In addition to the poster, augmented reality was used to reveal the actual bias behind the argument I was making, and the data I excluded on purpose.

You’ve been told caffeine is “bad” for you. You’ve been told around 8 hours of sleep per night is the “perfect amount”. But we all know, that combination is incompatible with nearly everyone’s lifestyle. Thankfully, that myth of “good sleep” and “natural energy” has been debunked!

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I used data and design to strengthen my argument, and convince the viewer through aesthetics that what I was certain of was true.

 
The caffeine data was manipulated to exclude a large amount of the data over three weeks. In my original poster, I only showed the days that were convenient to my argument. Clearly with this reveal, my argument is slightly flawed.

The caffeine data was manipulated to exclude a large amount of the data over three weeks. In my original poster, I only showed the days that were convenient to my argument. Clearly with this reveal, my argument is slightly flawed.

The data for the overall average was manipulated in an even sneakier way. For the poster, I changed the ratio of each variable from 1:1:1 to 1:2:2, to make the variables I wanted to look smaller to appear that way. Through manipulating the design itself I was able to support my argument. However, when the reveal happens, it becomes clear that it is worse than it initially appears.

The data for the overall average was manipulated in an even sneakier way. For the poster, I changed the ratio of each variable from 1:1:1 to 1:2:2, to make the variables I wanted to look smaller to appear that way. Through manipulating the design itself I was able to support my argument. However, when the reveal happens, it becomes clear that it is worse than it initially appears.