12 Myths about Self-Tracking

(Let me get a little provocative this time around and share some myths of self-tracking I’ve been playing with. I’d love to hear your thoughts about these and any other myths you might know about.)

Myth: You have to use technology.
Fact
: A good guideline is to use a tool that’s appropriate for the job. I know people who get good results using spreadsheets, and paper has some wonder affordances. (Read Malcolm Gladwell’s The Social Life of Paper for a fascinating analysis of air-traffic controllers’ paper-based system.) Then again, with large sets of data, visualization tools are invaluable.

Myth: Not everything can be measured.
Fact
: I suggest that, with a little (or maybe a lot) of creativity, you can come up with something you can measure for any experiment. Check out Alex’s post, How To Measure Anything, Even Intangibles. (Bonus: Do you have any that are giving you trouble? Let’s play “stump the blogger!”)

Myth: You have to be a scientist.
Fact
: While it probably helps to have a background in science, and better yet one in statistics, we can still do valuable work with rudimentary skills, given you design a strong experiment that can teach you something.

Myth: You have to start with a goal or theory.
Fact
: Sometimes we aren’t to the point where we have a working theory, but we are ready to start poking and prodding to see what might emerge. However, I’d argue that, at a minimum, we should always start with a question. What you ask might change, but it’ll get you moving. I’m still a believer that simply observing keenly can lead to awareness and eventually change.

Myth: Data-tracking is cold and dispassionate.
Fact
: Far from it! Just think back to an experiment of yours where you had an insight or surprise – how exciting was that? Curiosity is an emotion, after all, which drives our love of exploration, adventure, and discovery. Plus, exploring the world with a curious mind is great fun. At the same time, it takes a level of detachment to see results for what they are, especially if they rub up against something that we are attached to.

Myth: Self-experimenting is just for problem-solving.
Fact
: While addressing a specific concern is an excellent application of the self-quantification, I think of it more as a life-encompassing mindset. That is, a perspective on how to go about our lives, and one that asserts that our job of learning is never done.

Myth: Self-experimenting is easy.
Fact
: I’m sure you’ve noticed that it takes discipline to consistently track things about your life, and to do the thinking and learning that results from your data. Key is a strong desire to learn something and, ideally, to get an answer.

Myth: Self-experimenting is hard.
Fact
: The other side of the coin is that our work comes down to something simple: Thinking of a change to make, trying it while making observations, and then learning via reflection. Start out with something small that’s easy to measure, and then work up from there.

Myth: Citizens can’t do science.
Fact
: This one is about the broader view of the validity of citizens doing science, and was what I was getting at in my post Making citizen scientists – that this work applies especially well to the individual. (For some reasons, see the excellent comments at the bottom of that post.) Related to this are two other myths, the sample size of one is not valid and the results need to be clinical-grade ones.

Myth: It’s just for external things.
Fact
: Some of the richest territory to mine via experiments is your mind, behaviors, and mental models. Treating your thinking and behavior themselves as data is valuable, and can lead to fresh insights that you can use for improvement.

Myth: You have to use numbers.
Fact
: Scandalous, I know, but I’ve found that in some cases I can test out ideas without having to measure anything. However, I always make entries in my experimenter’s journal, which keeps me engaged and gives me something for later analysis.

Myth: It only applies to health.
Fact
: Related to my point above about the experimental mindset, I’ve had a lot of benefit from applying the thinking to all parts of my life. While health is the number one category in Edison, there are lots of other creative applications, including the social and work realms.

[Image from TPapi]

(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 matt@matthewcornell.org)

About Matthew Cornell

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 matt@matthewcornell.org
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9 Responses to 12 Myths about Self-Tracking

  1. R says:

    I enjoy the blog and like this post, I just have a little something to add. You mention that the myth that “a sample size of one is not valid.” I agree that this is a myth, or at least a poor arguement against self-tracking, but I’d like to point out two things. First, that sample size depends on the unit of analysis. And second, the ability to generalize depends on the population. Here is a little example. I read somewhere that a study found that subjects who ate an apple shortly before a meal consumed fewer calories overall (including the apple) than subjects who did not. If the study in question involved only one meal per individual, then the unit of analysis is the individual and the sample size is equal to the number of people in the study. Making certain assumptions, these results can then be generalized to whatever population the study’s subjects were drawn from. If wanted to see if this approach would help me reduce my caloric intake, I could flip a coin at every meal for some period of time to decide whether to eat an apple before my meal and record my total caloric intake at each meal. I could then analyze the data to see if I ate fewer calories when I ate an apple first. The sample size here is not 1, the sample size is the number of meals I tracked. However, since the population is only meals I ate (not those eaten by other people), I can’t generalize beyond myself.

    • Matthew Cornell says:

      I’m glad you liked it – thank you. Your example is enlightening. Your latter point – that it can’t be generalized beyond myself – makes sense to me. We’d need more people to try it. Your point about a time-based randomized test is really interesting. It seems like another kind of design, in addition to the two I’ve learned about and tried to share in Designing good experiments: Some mistakes and lessons: “Reversal or ABA designs” and “blocking” ones. Can you think of other major classes as applied to self-experimentation? My lack of statistics is in the way. Thanks for the great comment, R!

  2. Venkat says:

    Quantification may be an indication of direction of life Burt how much quantification is necessary.Some quantification are enough uto the level of sensing the direction for remedial action.When we see temp of the room we have ability to tweak it or slow it down.But in the open it is impossible except to cover up the body with suitable cloth or attire.

    • Matthew Cornell says:

      Good point. If I rephrase it as a Myth, it might be that we need to do a lot of quantifying to get results, or maybe that an experiment needs to go on to the end. The corresponding Fact might be that we need experiment only long enough to get useful results. I’m not sure I fully understand your point, though. Thanks for commenting.

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  5. Jonathan says:

    Myth: Only for data geeks
    Fact: Many self-tracking activities have become mainstream e.g. calorie counting, personal finance tracking. It is only a question of when the potential reward outweighs the cost of tracking.

    Myth: It’s unnatural
    Fact: We love to hate Drago (Rocky IV) because he seemed so artificial and manufactured. We despise the PUA community because their routines are excessively contrived. The fact is however, we are all natural born experiments and without this trait we would never learn to walk or talk. The only difference is we generally approach our experiments haphazardly and without a clear goal. It is only natural to attempt to systemize these experiments in order to improve their effectiveness.

    Myth: You need perfect data
    Fact: Almost any attempt at self-tracking is going to encounter data imperfections, especially where manual intervention is required. With a large enough sample size however these imperfections become less important as it is the underlying trends which are most important.

    • Matthew Cornell says:

      Great myths, Jonathan! Nice generalization about rewards vs. benefits, which is also true of any habit change. Your second myth expresses nicely our intrinsic need to explore – thanks for that. And I’m really glad you mentioned perfection – one of my risks is letting it get in the way of experimenting (and of everything, actually). It’s what I was trying to get in my section “Doing something, even if it’s imperfect, is better than nothing” in my post “Designing good experiments” above. Thanks again.

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