An area of science is an ecosystem in the sense that research builds on other research. In an ordinary ecosystem the animals and plants need each other. Different organisms add different things. Their contributions fit together. In a healthy scientific ecosystem, different types of research add different things and fit together.
Personal science (science done to help yourself) differs greatly from professional science (science done as a job). The big differences help personal science and professional science benefit from each other. They are likely to benefit each other because they have interlocking strengths and weaknesses. Personal science is fast (experiments can be started quickly), has great endurance (experiments can last years), cheap, and intensely focussed on benefit. Professional science has none of these features, but it has other features that personal science lacks: it is “wealthy” (allowing expensive equipment and tests), peer-reviewed, and not intensely focussed on benefit, which allows studies without obvious value. These differences suggest that a system that contains both kinds of science is going to function better than a system with only one kind. Peer review, for example, is a helpful filter but may also suppress the diversity of ideas that are tested. Which implies that not all science should be peer-reviewed.
The relation between personal and professional science somewhat resembles the relation between animals (= personal science) and plants (= professional science). Animals and plants are very different, as are personal and professional science. Animals move faster than plants; personal science moves faster than professional science. Animals range more widely than plants. Likewise, a personal scientist can test a much wider range of treatments than a professional scientist. If you want to sleep better, for example, you can try almost anything. Professional scientists cannot try almost anything. For example, they cannot test treatments considered “crazy”.
Animals and plants helped each other evolve, in the sense of diversifying to exploit new habitats. Animals helped plants exploit new habitats because they increased seed dispersal. This helped plants “test” more locations, helped them survive difficult circumstances such as drought (because some places are drier than others), and reduced competition between seeds (allowing more resources to be devoted to overcoming bad features of new places). Animals are like catalysts that speed up the combination of old plant and new environment to yield new plant. Likewise, plant evolution helped animals evolve because new plants in new places provided more food, more diverse food, and more places to live.
It is likely that personal science and professional science will help each other “evolve” (e.g., solve problems). Personal science wouldn’t function well without professional science. For example, statistical packages, which help personal scientists, wouldn’t exist without professional science. In the other direction, personal science can help professional science “evolve” (e.g., solve problems, build better theories) in two ways. One is idea generation, especially discovery of new cause-effect relationships. Personal scientists can easily do large amounts of trial and error. They can easily test many “crazy” (= low-probability-of-success )treatments, one after the other, until they find something that works. Professional scientists cannot do this sort of thing, which in the world of professional science has a derogatory name: fishing expedition. The other way personal science can help professional science involves idea application. Personal science can tailor ideas from professional science to individual circumstance. Professional scientists don’t like to do this. They would rather do a big study in which all subjects are treated alike. Making better practical use of ideas from professional science is what Richard Bernstein did when he invented home blood glucose monitoring. He made better use of already-known cause-effect relationships.
I have not heard scientists talk about science as an ecosystem. If they did, it might cut down on the dismissiveness (correlation does not equal causation, the plural of anecdote is not data, etc.), evidence snobbery, and one-way skepticism.
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Sign in nowBut “correlation does not equal causation” is a vital truth, the ignoring of which has led to two generation’s worth of drively health advice/instruction to the population.
Obviously, when different entities interact in an ecosystem, they co-evolve.
However, these interactions are not always as benign as your portrayal. The players may be also be predators and prey, for example.
And in the case of individuals and BigScience (or BigMedicine, or BigPharma), they may be rivals, especially when the large institutions dismiss N=1 evidence as “anecdotal.”
Whenever I have to choose between a large expensive study and my own N=1 experiences, I will feel comfortable going with N=1.
Seth,
You recently posted about someone who experienced breakouts and [Irish] dairy. I think I have noticed this with Fage [full fat], but I have not tightly controlled my variables. Have you had any comments like this before? Would you suspect a difference between homemade yogurt (I like strained) and Fage?
Me too. But I am saying here that this choice — deciding which is better — doesn’t do justice to the relationship. For example, people who do N=1 studies may be able to learn from large expensive studies. My first self-experimentation of any interest was a comparison of two Big Pharma treatments for acne. One worked, the other didn’t. It was helpful to know that one worked.
I disagree. I would call “correlation does not equal causation” a phony critique. It appears to be helpful but actually isn’t because it ignores the complexity of specific cases (the extent to which a correlation implies a specific causation depends on the plausibility of alternative explanations — about which no blanket statement is possible). I explain the poor health advice of recent decades as due to poor science — the health scientists involved (e.g., the ones studying weight control) are unable to make progress. A large fraction of them do experiments — e.g., the large clinical trials that “correlation does not equal causation” advocates are fond of. Their lack of progress cannot be explained by a failure to heed that idea.
But much of the dud health advice – particularly but not solely dietary advice – is sourced not in large RCTs but in correlational studies, the sort of things that reveal (to quote an old joke) that it’s people who wear bras whom are most prone to breast cancer.
John, several studies have pointed to dairy as one cause of acne. Even in America.
Seth, for me this is a very profound and astute insight. It is also expressive of what I think should be an ideal for real scientific discovery and progress.
The caveat here, as I see it, is that the “professional” side of scientific study/ experimentation/inquiry must be done more honestly and not be influenced or corrupted by special interests and/or those with an axe to grind.
All too often, it appears to me that the “professionals” are engaged in the opposite of true scientific method; i.e. the research/inquiry is not conducted with a focus of trying to disprove a hypothesis, but rather to seek a specific result for financial or other material gain. (Have any of those guys ever heard of Karl Popper?) I’m sorry to say that pharma company research / drug development practices come to mind as one glaring example.
So at this point I have to side with Jim P. on the virtue of N=1, but I certainly agree that professional science could (and should) be an invaluable aid to progress, but only if the corrupting incentives of $$ can be removed from the equation, or at least attenuated.