What if you don’t like the data?
October 21, 2010
In collecting data about ourselves we naturally encounter information that is unpleasant or unwelcome. This can range in scope from the prosaic (“I’m not losing weight fast enough”) to the the profound (“I just found out I have cancer.”)
While this is a pervasive aspect of being human – resolving the conflict between What Is and What Ought To Be (apologies to David Hume) – I want to play with going beyond the general case of dealing with bad news, such as the oft-mentioned Kübler-Ross model (Shock, Denial, Anger, Bargaining, Depression, Testing, and Acceptance). I wonder if we can get guidance and some measure of equanimity by framing the data from a scientific mindset. Let’s see.
A hallmark of science is striving to view results objectively – what I call a “healthy sense of detachment.” I’m humbled by how Victor Frankl puts it in his book Man’s Search for Meaning:
Cold curiosity predominated even in Auschwitz, somehow detaching the mind from its surroundings, which came to be regarded with a kind of objectivity.
This is not to say you don’t acknowledge or experience your emotions; far from it. In fact, your reactions and feelings are themselves data. But it helps to think of them as playing a separate role.
The act of tracking
We can draw comfort from keeping an experimenter’s journal, which is your record of not just the data but of your experimentation process itself. While this is a common tool in the self-help literature, our take here is inspired by what Jim Collins termed a bug called Jim – the idea of studying and gaining insights from the odd and fascinating creatures that we are. This further reinforces objectivity.
Doing an analysis of the data can activate the rational mind, rather than being mired in reactive mode from the bad news. To do this you might ask,
- What was my theory going in?
- Is the data correct?
- What is it telling me?
- Is my interpretation valid?
- What are my options moving forward?
Action through exploration
One of the two things we can control – our thinking and behavior – acting on the data is empowering. If we can keep our curiosity intact, there is always more to discover. Here are some possible steps to follow:
- Classify it as a problem.
- Give it a label.
- Examine the situation.
- Plan solutions.
- Execute them.
Control and acceptance
Constraints simplify. In our model of the situation the data describe, we have a finite set of things we can do. As students of self-quantification it is logical to identify realms of control, and to take some comfort from concrete reality – that is, facing the brutal facts. I suppose this is my attempt to make a secular version of the saccharine Serenity Prayer. This perspective doesn’t mean you have to be passive, though. Remember that self-tracking is about the power of discovery.
Finally, rather than the cold, solitary scientist portrayed on TV in a sterile white lab coat, in reality the best science is done in collaboration with others. Soliciting the help of friends, family, and fellow experimenters can enable identifying biases, fleshing out our analysis, and importantly, giving emotional support. This makes it so that it’s not just you and the data. I love Lewis Thomas’s observation that he could tell when something important was going on in an experimental lab by the laughter. And being able to laugh in the face of nasty data – that’s a true gift.
[Image from Okinawa Soba]
(Matt is a terminally-curious ex-NASA engineer and avid self-experimenter. His projects include developing the Think, Try, Learn philosophy, creating the Edison experimenter’s journal, and writing at his blog, The Experiment-Driven Life. Give him a holler at firstname.lastname@example.org)