Recently I was discussing my dissertation progress with another volunteer at the bird banding station. “I have all the data,” I said, “I just need to figure out how to spin it.”
She looked taken aback. “Well, it’s data,” she said. “It’s information. You don’t spin it; it just is.”
“Right,” I agreed quickly, in my best Objective Scientist voice. “Of course.”
I thought about this exchange a lot over the next few weeks. It had been a while since I had talked about my research at length with a non-scientist, and her reaction to my word choice made an impression. Why had I said “spin”? Did I mean “spin”?
I paid more attention than usual to the word choices my colleagues made, and quickly realized that we all talk about spinning our data. We also talk about interpreting our data, and framing our data: similar and related concepts, but not exact synonyms for spinning the data. “Spinning” sounds underhanded, deceitful. It sounds like we are making the data say what we want it to say. Shouldn’t the data speak for itself?
Data is information, yes; but it usually doesn’t speak for itself. Data needs help to speak. A good graph can show anyone who looks at it some conclusion—but someone had to make that graph first, make decisions about its design that affect what conclusion, what story, a viewer will see. And a lot of the sorts of data we get in ecology and evolution research need some background and interpretation to tell any story.
Imagine, for example, a study on the relationships of different species of turtles.
This seems pretty straightforward: the results will be a diagram of the species relationships. Simple. But one could tell a number of different stories about it: one might be, “Turtles are related the way we would have predicted based on their shell shapes.” Or, “Turtles have evolved pointy heads multiple separate times: pointy heads must be important.” Or, “Turtles are so closely related that we need new genomics methods to really tell how they are related, because the current methods aren’t working well.” Or, “The Brontosaurus-necked Turtle should be in its own genus.”
These are all objective conclusions one could make from data; but they require a good background knowledge, and an idea about what to do with the data. More, they change how a viewer looks at the data: they tell what to look for, what patterns to evaluate.
In my own research, I’ve struggled with this most with one particular result. In general terms, I found that the juncos do not do what I was pretty certain they would do. When I tell people about it, I’ve tended to say: “I thought they would do [thing], but they don’t.” This is an accurate presentation of my data, but not a very informative story. Better would be: “Juncos elsewhere do [thing], but the ones I studied don’t, probably because of [reason].” Or even, “I discovered that juncos pursue [strategy], so that some do [thing] while mine do [alternative thing].”
When scientists say we are spinning our data, we mean this: telling a story about the data. We mean interpreting the data and framing it with relevant background, and presenting it as a whole concept to an audience. I think we like to pretend, sometimes, that we simply collect data and pass it on to the public as pure results (well, or to each other, at least; I’m aware that the public probably doesn’t make a habit of reading, say, Behavioral Ecology and Sociobiology). But that leaves out an important part of being a scientist. We don’t just email each other Excel spreadsheets full of data when we finish a project; we write papers with sections titled “Introduction” and “Discussion.”
The reason that becoming a good scientist takes a lot of time, and studying, and trial-and-error, is that it isn’t as simple as collecting the data (which certainly isn’t simple either): you have to know what story to tell with that data. Turning the data into a story—spinning it—isn’t deceitful: it’s how you explain to everyone else what you found.