Does Malcolm Gladwell’s brand of storytelling have any lessons for data scientists? Or is it unscientific pop-sci pablum?
Gladwell specializes in uncovering exciting and surprising regularities about the world — you don’t need to reach a lot of people to spread your ideas (The Tipping Point), your intuition wields more power than you imagined (Blink), and success depends on historical or other accident as much as individual talent (Outliers).
[Gladwell] excels at telling just-so stories and cherry-picking science to back them. In “The Tipping Point” (2000), he enthused about a study that showed facial expressions to be such powerful subliminal persuaders that ABC News anchor Peter Jennings made people vote for Ronald Reagan in 1984 just by smiling more when he reported on him than when he reported on his opponent, Walter Mondale. In “Blink” (2005), Mr. Gladwell wrote that a psychologist with a “love lab” could watch married couples interact for just 15 minutes and predict with shocking accuracy whether they would divorce within 15 years. In neither case was there rigorous evidence for such claims. [Christopher Chabris, The Wall Street Journal]
On his blog, Chabris further critiques Gladwell’s approach, defining a hidden rule as “a counterintuitive, causal mechanism behind the workings of the world.” Social scientists like Chabris are all too well aware that to really know what’s happening causally in the world we need replicable experimentation, not cherry-picked studies wrapped up in overblown stories.
Humans love hidden rules. We want to know if there is some counterintuitive practices we should be following, practices that will make our personal and business lives rock.
Data scientists are often called upon to discover hidden rules. Predictive models potentially combine many more variables than our puny minds can handle, often doing so in interesting and unexpected ways. Predictive and other correlational analyses may identify counterintuitive rules that you might not follow if you didn’t have a machine helping you. We learned this from Moneyball. The player stats that baseball cognoscenti thought worked for identifying the best players turned out to be less effective than stats identified by predictive modeling in putting together a winning team.
I am sympathetic to Chabris’ complaints. When I build a predictive model, a natural urge is to deconstruct it and see what it is saying about regularities in our world. What hidden rules did it identify that we didn’t know about? How can we use those rules to work better? But the best predictive models often don’t tell us accurate or useful things about the world. They just make good predictions about what will happen — if the world keeps behaving like it behaved in the past. Using them to generate hidden, counterintuitive rules feels somehow wrong.
Yet the desire for good stories won’t go away. Neither will the challenges of figuring out causal realities using whatever data we have on hand. We need stories that don’t dispense with science.
How about counterintuitive examples as stone soup?
As those of you who are social scientists surely already know, ideas are like stone soup. Even a bad idea, if it gets you thinking, can move you forward. For example: is that 10,000 hour thing true? I dunno. We’ll see what happens to Steven Levitt’s golfing buddy. (Amazingly enough, Levitt says he’s spent 5000 hours practicing golf. That comes to 5 hours every Saturday . . . for 20 years. That’s a lot of golf! A lot lot lot lot of golf. Steven Levitt really really loves golf.) But, whether or not the 10,000-hour claim really has truth, it certainly gets you thinking about the value of practice. Chris Chabris and others could quite reasonably argue that everyone already knows that practice helps. But there’s something about that 10,000 hour number that sticks in the mind.
When we move from heuristic business rules to predictive models there’s a need to get people thinking with more depth and nuance about how the world works. Telling stories with predictive or other data analytic models can promote that, even if the stories are only qualifiedly true.
If the structure and outputs of a predictive model can be used to get people thinking in more creative and less rigid ways about their actions, I’m in favor. Doesn’t mean I’m going to let go of my belief in the ideal of experimentation or other careful research designs for figuring out what really works, but it does mean maybe there’s some truth to the proposition that data scientists should be storytellers. Finding and communicating hidden rules a la Gladwell can complement careful science.