Seth Roberts on Personal Science

In the IEEE Spectrum, Paul McFedries, the author of Word Spy, writes about new words generated by new kinds of science made possible by cheap computing.

Perhaps the biggest data set of all is the collection of actions, choices, and preferences that each person performs throughout the day, which is called his or her data exhaust. Using such data for scientific purposes is called citizen science. This is noisy data in that most of it is irrelevant or even misleading, but there are ways to cull signal.

That’s not my understanding of what citizen science means. I’ve seen it used when non-scientists (”citizens”) help professional scientists. The Wikipedia definition is

projects or ongoing program of scientific work in which individual volunteers or networks of volunteers, many of whom may have no specific scientific training, perform or manage research-related tasks such as observation, measurement or computation

Bird-watching, for example.

My self-experimentation is not citizen science. I am not doing it to help a professional scientist nor as part of a project. I do it to help myself — in contrast to professional science, which is a job. Almost all self-experimentation by professional scientists and doctors has been done as part of their job.

So let me coin a term that describes what I do: personal science. Science done to help the person doing it.

I believe personal science will grow enormously, for several reasons:

1. Lower cost. The necessary equipment, such as software, costs less and less. I use R, which is free.

2. Greater income. People can afford more stuff.

3. More leisure time.

4. More is known. The more you know, the more effective your research will be. The more you know the better your choice of treatment, experimental design, and measurement and the better your data analysis.

5. More access to what is known. For example, Dennis Mangan discovered via the internet that niacin had cured restless leg syndrome.

6. Professional scientists unable to solve problems. They are crippled by career considerations, poor training, the need to get another grant, desire to show off (projects are too large and too expensive), and a Veblenian dislike of being useful. As a result, problems that professionals can’t solve are solved by amateurs. The best-known example is the invention of blood-glucose self-monitoring by Richard Bernstein, who was not a doctor when he invented it.

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8 Responses to Seth Roberts on Personal Science

  1. Matthew Cornell says:

    Thanks, Seth. Reply on your blog. (For fellow QS readers: This is cross-posted at Seth’s blog: Seth’s blog » Blog Archive » Personal Science.)

    • Cemalettin says:

      Sample size isn’t everything. Sure, a study with n=1 isn’t scientific proof . Nor is any other study, in my experience. Scientific proof has always required many studies. New scientific ideas have very often started with n = 1 experiments or observations. Later, larger experiments or observations were done. Both the initial n=1 observation and the later n = many observations were necessary for the new idea to be discovered and confirmed.2. The history of biology teaches there are few exceptions to general rules. See any biology textbook. For example, a textbook might say lymphocytes fight germs . This means no serious exceptions have ever been found to that rule. So, as matter of biological history, the person who managed to figure out one particular lymphocyte does turn out to have figured out what they all do. Biology textbooks have thousands of statements like lymphocytes fight infection meaning that this sequence of events (you can generalize from one to all, or nearly all) has happened thousands of times. There is no shadow hidden history of biology that teaches otherwise.

    • BudMan says:

      Being a follower of both QS and paleo I’ve thgouht about this topic for a while, and I feel that the distinction between the Paleo and QS movements is really the difference between explanation (theories) and observation (data). Science needs theories, without them it is not science, it is, at best pre-science or just the initial steps of the scientific method. Ironically, the data-driven mode of exploration that QSers engage in is often less scientific than the Paleo folks who rely much less on quantification. This is a huge meme in the paleo community, they have a deep distrust of observational studies, and they blame unthoughtful and premature quantification for the prevalence of erroneous theories such as the lipid hypothesis.What QSers should take away from the Paleo movement is that if you want to make any type of actual progress you need to base your data collection and quantification on a foundation of reasonable hypotheses and use experimentation to iterate upon them until you arrive at robust theories. In other words, QSers should be doing science not stamp collecting.

  2. This post is quite related to Matt’s Feb 4 “citizen scientist” post (FYI Matt, I looked for your response on Seth’s blog but couldn’t find it).

    A comment on the McFedries excerpt, the phrase “data exhaust” is interesting / helpful as a visual metaphor but not accurate in terms of the relationship between our actions in the physical world and the quantification of those actions. The data that we are throwing off is not a byproduct of our actions – it’s more like a history with (potentially) infinite granularity, right?

  3. Matthew Cornell says:

    [Sorry, Misha - it's there now. Not too much to say right now.]

    > data exhaust

    Exhaust usually means waste that’s a byproduct of production. However, in our case data is the *means* of self-improvement. It’s like a catalyst for making a change in ourselves. Plus, unlike exhaust, it has value after its use. While factories may capture waste products for other uses, they don’t treat the waste as intrinsically useful. That’s a big difference, I agree. Nicely provocative phrase, though. Not sure about the granularity you talk about.

  4. Pingback: The Big Bucket Personal Informatics Data Model | Quantified Self

  5. SVideo says:

    I completely agree. Most people don’t realize that scientists have incredibly poor training. Unlike other professions, scientists spend very little time actually going to school. And despite that fact, they find themselves hideously overpaid at every stage of their careers.

  6. Bernita says:

    Very interesting talk. I am just cuirous how someone can claim a study conducted with a sample size of one is 100 times better than someone else’s study. I do not know anything about the other study mentioned, but I do know that a study based on n=1 cannot be considered scientific proof. And sure, he hears from people who have lost weight drinking the sugar water he prescribed, but it is quite possible there are 100 times as many people who didn’t email him because they didn’t see any positive results and decided to try something else. I think the QS stuff is very interesting and helpful on a personal level, but it seems like a stretch to generalize your results to others.

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