As a statistician who often needs to explain methods and results of analyses to non-statisticians, I have been receptive to the influx of literature related to the use of storytelling or a data narrative. (I am also aware of the backlash related to use of the word “storytelling” in regards to scientific analysis, although I am less concerned about this than, say, these scholars.) As a teacher of data analysis, the use of narrative is especially poignant in that it ties the analyses performed intrinsically to the data context—or at the very least, to a logical flow of methods used.
I recently read an article posted on Brain Pickings about the psychology behind great stories. In the article, the author, Maria Popova, cites Jerome Bruner’s (a pioneer of cognitive psychology) essay “Two Modes of Thought”:
Each of the ways of knowing, moreover, has operating principles of its own and its own criteria of well-formedness. They differ radically in their procedures for verification. A good story and a well-formed argument are different natural kinds. Both can be used as means for convincing another. Yet what they convince of is fundamentally different: arguments convince one of their truth, stories of their lifelikeness. The one verifies by eventual appeal to procedures for establishing formal and empirical proof. The other establishes not truth but verisimilitude.
The essence of his essay is that, as Popova states, “a story (allegedly true or allegedly fictional) is judged for its goodness as a story by criteria that are of a different kind from those used to judge a logical argument as adequate or correct.” [highlighting is mine]
What type of implications does this have on a data narrative, where, in principle, both criteria are being judged? Does one outweigh the other in a reader’s judgment? How does this affect reviewers when they are making decisions about publication?
My sense is that psychologically, the judgment of two differing sets of criteria will lead most humans to judge one as being more salient than the other. Presumably, most scientists would want the logical argument (or as Brunner calls it, the logico-scientific argument) to prevail in this case. However, I think it is the story that most readers, even those with scientific backgrounds, will tend to remember.
As for reviewers, again, the presumption is that they will evaluate a paper’s merit on its scientific evidence. But, as any reviewer can tell you, the writing and narrative presenting that evidence is in some ways as important for that evidence to be believable. This is why great courtroom attorneys spend just as much time on developing the story around a case as marshaling a logical argument they will use to entice a jury.
There is little guidance for statisticians, especially nascent statisticians, about what the “right” degree of paradigmatic and logico-scientific argument should be when writing up data analysis. In fact, many of us (including myself) do not often consider the impact of a reader’s weighing of these different types of evidence. My training in graduate school was more focused on the latter type of writing, and it was only in undergraduate writing courses and through reading other authors’ thoughts about writing that the former is even relevant to me. Ultimately there may be not much to do about how reader’s perceive our work. Perhaps it is as Sylvia Plath wrote about poetry, once a poem is made available to the public, the right of interpretation belongs to the reader.”