Why storytelling is our best tool in disambiguating fact from fiction
This summer, I had the great privilege of attending EyeO (June 3–8 2018). Innumerable topics that encompass the intersection of Art, Technology, and Data were covered, but one common thread has left an imprint on my brain. That is: the Sisyphean 21st century task of disambiguating fact from fiction. That’s right…
I’d love to share a few of the lecturers who touched upon this topic and forever changed my understanding of the 2018 landscape of fact, fiction, and storytelling’s role in deciphering one from the other.
PART 1: NUMBERS ARE MALLEABLE
On the first day, we discussed climate science at length. We (a very self aware room of liberal, number-crunching, data-visualization-making, coastal-living, self-ascribed nerds) attempted to break down the problems with human psychology. We looked at the facts, stats, charts, and graphs; then investigated the human power of denial, dissonance, disincentivization, and the hurdles of behavioral change. After 6 hours of discussion, ideation, and reflection, feeling a bit helpless, we ended with questions that I kept with me throughout the next 3 days of lectures:
Why don’t people believe statistics?
Are stories more powerful than numbers?
Why is denial more powerful than behavioral change?
Why do lies travel faster than truth?
…And what should we do about this?
The next day, Amanda Cox enlightened us with her talk These Lines Are The Same. She showed us that data, even in simple bar graphs, can be misinterpreted depending on the viewer’s own bias. She bravely revealed to us that in her department The Upshot at The New York Times they struggle with how to best represent datasets objectively. They experiment in meaningful and educational ways. In one example she showed data from the US unemployment report. The article allows readers to look at the chart with ‘Democratic Goggles’ and ‘Republican Goggles.’
The numbers are the same, but they can easily be bent to the will of anyone with an agenda.
Then she humorously showed us our flaws in clinging to round numbers. She drove the point home with a series of charts, one here showing the likelihood that someone in the ER gets checked for a heart attack, according to their age. As Amanda points out, “nothing radical changes from the age of 39-and-three-quarters and 40, yet here is the data: