Causal thinking with stories compared to statistical thinking with numbers

Our evolution equipped us to create causal explanations for events much more readily than to grasp underlying statistical explanations, to use the terms of Daniel Kahneman, Thinking, Fast and Slow (Farrar, Straus & Giroux 2011). Causal explanations, often in the form of a narrative, explain what has happened by people or understood forces doing explicable things to bring it about. “Chris had a good day in court, so we won.” “Mega Corp.’s CEO had to close the deal before year end to get his bonus, so they conceded several points.”

Statistical thinking, by contrast, derives conclusions about individual cases from properties of categories and ensembles. Chris’s company typically prevails on 75 percent of its cases. Or, more than 60 percent of deals that reach a certain stage go on to close.

Savings attributed to the start of a new process or new software or new training often exemplify a causal explanation. “We did X and Y followed.” Our quick System 1 minds favor neat patterns recognized and stories fit snugly together. In fact, it weaves them at the snap of a finger and out of few facts. But it may often be wrong or fanciful. Our slower System 2 minds can turn to probabilities and bigger-picture explanations.

The improvement might well be statistical: the random walk of events or regression to the mean, the Hawthorne effect, selective attention, confirmation or cognitive bias – the blind stumble of chance.