Data never exists in a vacuum
We like to believe that data (whether big or small) holds the answers we are looking for. It’s just as romantic a notion that it does, as are the notions that we tend to reject, like how feelings are unreliable ways to make decisions.
Data, however, never exists in vacuum. Data is collected in response to a question. And the way you ask a question can affect the kinds of responses you get. It’s why we try not to ask questions like, “So, you don’t mind if we go to Rigatoni’s for dinner?” if we’re trying to ask the honest question of, “Where would you like to go for dinner?”
Data has context, subtext and super text. Data is collected for a reason, even if that reason isn’t always well-articulated. Those reasons are informed by the asker’s feelings, past work and past experiences (subtext). They are also informed by the asker’s world, their society and culture (supertext). And while we might be trying our darnedest to avoid bias; we can’t ignore the -texts and pretend they’re not actually there and that they don’t affect us.
When someone presents “objective” data to you, and you interpret it as though context, subtext, and supertext don’t exist, you run the risk of using it blindly, and, therefore, inappropriately. Simplistic explanations tend to strip away context, supertext and subtext because it’s the supposed way to get to the “core” of the information — to make it “accessible”. All it does is create confusion and blind thinking.
The question no one is asking isn’t, “Where did this data come from,” but rather, “Why does this data even exist in the first place?”
This is where research meets practice. It’s not about the statistics, or even the methods. It’s about asking the questions that will get you to your answer. Learning to ask is what the Critical Mass Mentorship is about.
Find out more at http://criticalmass.ninja
