I’m with Errol: On flypaper, photography, science, and storytelling

January 3, 2018
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(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

[image of a cat going after an insect]

I’ve been reading this amazing book, Believing is Seeing: Observations on the Mysteries of Photography, by Errol Morris, who, like John Waters, is a pathbreaking filmmaker who is also an excellent writer.

I recommend this book, but what I want to talk about here is one particular aspect of Morris’s work which brings together a bunch of things I’ve been thinking about in the past few years, regarding how we understand and communicate about the world.

While reading Morris’s book I came across this quote:

Photographs attract false beliefs the way flypaper attracts flies.

But it’s more than that. Flypaper doesn’t just attract flies, it also traps them. Similarly, photographs, by having many appealing hooks, attract false beliefs, and photographs also can sustain false beliefs by appearing to supply evidence to confirm all sorts of erroneous notions.

This connects to two of our recurring themes:

1. The immutability of data and stories. Recall that Thomas Basbøll and I argued that we learn from stories because of their anomalousness and their immutability: a story is anomalous when it refutes some existing theory we had about the world, and it’s immutable to the extent that it has an existence independent of these theories.

In my terminology, stories, which have immutable, telling details, are different from parables, whose details can be adapted to fit the message they are sending. In our other paper, Basbøll and I criticized plagiarist Karl Weick in part because, by changing the specifics of the story he was plagiarizing, he was allowing the story to convey a message that was in many ways opposite to that of the original telling. Weick was turning the story into a parable, and his plagiarism facilitated that transformation by making it harder for readers to learn from the original source.

2. A central message of Morris in his book on photography, and in his work more generally, is that images (or, more generally, statements and other data) can mislead, but if we look at them carefully and with skepticism, we can discover things we never would’ve learned without careful inspection and interrogation.

This is related to our emphasis on the complementarity of two statistical methods that are often taken as competing: exploratory data visualization, and complex Bayesian modeling.

We learn by looking hard at an image (or transcript, or other form of data), but we can learn more in the context of a model—and we can try out model after model to see how they fit the data. This was how Morris’s breakout movie, The Thin Blue Line, worked. The notorious reconstructions in that movie were, essentially, posterior predictive checks. And this in turn connects to our idea of storytelling as predictive model checking: storytelling is a creative exploration of possibilities and in that sense can be considered as a form of deductive reasoning, or working out of consequences.

3. Finally, let’s return to the subject of false beliefs that are inspired by, and appear to be supported by, data. This has come up a lot recently with debunkings of famous studies that had purported to show that being primed with elderly-related words makes you walk more slowly, or that elections are determined by shark attacks, or that subliminal smiley faces can have big effects on political attitudes on immigration, or that 20% of women change their vote preferences based on the time of the month, or that beautiful parents are more likely to have girls, and a million other things. What was striking about these cases is that, once people had what appeared to be convincing evidence of their theories, they refused to back down—even after careful investigation of the evidence made it clear that these theories were not supported at all.

A theory is created out of misleading evidence, the evidence is knocked down, the theory remains. Again, this is a theme that should be familiar to Errol Morris fans, along with the followup, which is when defenders of the unsupported theory start to argue that the details of the evidence don’t really matter.

The post I’m with Errol: On flypaper, photography, science, and storytelling appeared first on Statistical Modeling, Causal Inference, and Social Science.



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