But what we think we know can be influenced by our assumptions about what we think is true, too. It's all too easy to look at data and interpret it in a way that makes sense to us, even if there are multiple possible interpretations. This can be a particular problem in social science, when we've got a favorite theory and the data can be seen to confirm it; this is perhaps easiest to notice if you yourself aren't wedded to any of the theories. But it's also true in biology. It is understandable that we want to assert that we now know something, and are rewarded for insight and discoveries, rather than more humbly hesitating to make claims.
The other day I was listening to the BBC Radio 4 program Analysis on the charitable impulse. Why do people give to charity? It turns out that a lot of psychological research has been done on this, to the point that charities are now able to manipulate us into giving. If you call your favorite NPR station to donate during a fund drive, e.g., if you're told that the caller just before you gave a lot of money, you're more likely to make a larger donation than if you're told the previous caller pledged a small amount.
|A 1931 advertisement for the British charity, Barnardo's Homes; Wikipedia|
Or, if an advertisement pictures one child, and tells us the story of that one child, we're more likely to donate than if we're told about 30,000 needy children. This works even if we're told the story of two children, one after the other. But, according to one of the researchers, if we're shown two children at once, and told that if we give, the money will randomly go to just one of the children, we're less likely to give. This researcher interpreted this to mean that two is too many.
But there seem to me to be other possible interpretations given that the experiment changes more than one variable. Perhaps it's that we don't like the idea that someone else will choose who gets our money. Or that we feel uncomfortable knowing that we've helped only one child when two are needy. But surely something other than that two is too many, given that in 2004 so many people around the world donated so much money to organizations helping tsunami victims that many had to start turning down donations. These were anonymous victims, in great numbers. Though, as the program noted, people weren't nearly as generous to the great number of victims of the earthquake in Nepal in 2015, with no obvious explanation.
The researcher did seem to be wedded to his one vs too many interpretation, despite the contradictory data. In fact, I would suggest that the methods, given what were presented, don't allow him to legitimately draw any conclusion. Yet he readily did.
The Food Programme on BBC Radio 4 is on to the microbiome in a big way. Two recent episodes (here and here) explore the connection between gut microbes, food, and health and the program promises to update us as new understanding develops. As we all know by now, the microbiome, the bug intimates that accompany us through life, in and on our body, may affect our health, our weight, our behavior, and perhaps much more. Or not.
|Pseudomonas aeruginosa, Enterococcus faecalis and Staphylococcus aureus on Tryptic Soy Agar. Wikipedia|
Obesity, asthma, atopy, periodontal health, rheumatoid arthritis, Parkinson's, Alzheimer's, autism, and many many more conditions have been linked with, or are suggested to be linked with, in one way or another, our microbiome. Perhaps we're hosting the wrong microbes, or not a diverse enough set of microbes, or we wipe the good ones out with antibiotics along with the bad, or with alcohol, and what we eat may have a lot to do with this.
One of the researchers interviewed for the program was experimenting with a set of identical twins in Scotland. He varied their diets having them eat, for example, lots of junk food and alcohol, or a very fibrous diet, and documented changes in their gut microbiomes which apparently can change pretty quickly with changes in diet. The most diverse microbiome was associated with the high fiber diet. Researchers seem to feel that diversity is good.
Along with a lot of enthusiasm and hype, though, mostly what we've got in microbiome research so far is correlations. Thin people tend to have a different set of microbes than obese people, and people with a given neurological disease might statistically share a specific subset of microbes. But this tells us nothing about cause and effect -- which came first, the microbiome or the condition? And because the microbiome can change quickly and often, how long and how consistently would an organism have to reside in our gut before it causes a disease?
There was some discussion of probiotics in the second program, the assumption being that controlling our microbiome affects our health. Perhaps we'll soon have probiotic yogurt or kefir or even a pill that keeps us thin, or prevents Alzheimer's disease. Indeed, this was the logical conclusion from all the preceding discussion.
But one of the researchers, inadvertently I think, suggested that perhaps this reductionist conclusion was unwarranted. He cautioned that thinking about probiotic pills rather than lifestyle might be counterproductive. But except for factors with large effects such as smoking, the effect of "lifestyle" on health is rarely obvious. We know that poverty, for example, is associated with ill health, but it's not so easy to tease out how and why. And, if the microbiome really does directly influence our health, as so many are promising, the only interesting relevant thing about lifestyle would be how it changes our microbiomic makeup. Otherwise, we're talking about complexity, multiple factors with small effects -- genes, environmental factors, diet, and so on, and all bets about probiotics and "the thinness microbiome" are off. But, the caution was, to my mind, an important warning about the problem of assuming we know what we think we know; in this case, that the microbiome is the ultimate cause of disease.
The problem of theory
These are just two examples of the problem of assumption-driven science. They are fairly trivial, but if you are primed to notice, you'll see it all around you. Social science research is essentially the interpretation of observational data from within a theoretical framework. Psychologists might interpret observations from the perspective of behavioral, or cognitive, or biological psychology, e.g., and anthropologists, at least historically, from, say, a functionalist or materialist or biological or post-modernist perspective. Even physicists interpret data based on whether they are string theorists or particle physicists.
And biologists' theoretical framework? I would suggest that two big assumptions that biologists make are reductionism and let's call it biological uniformitarianism. We believe we can reduce causation to a single factor, and we assume that we can extrapolate our findings from the mouse or zebrafish we're working on to other mice, fish and species, or from one or some people to all people. That is, we assume invariance rather than that what we can expect is variation. There is plenty of evidence to show that by now we should know better.
True, most biologists would probably say that evolutionary theory is their theoretical framework, and many would add that traits are here because they're adaptive, because of natural selection. Evolution does connect people to each other and people to other species, it has done so by working on differences, not replicated identity, and there is no rule for the nature or number of those differences or for extrapolating from one species or individual to another. We know nothing to contradict evolutionary theory, but that every trait is adaptive is an assumption, and a pervasive one.
Theory and assumption can guide us, but they can also improperly constrain how we think about our data, which is why it's good to remind ourselves from time to time to think about how we know what we think we know. As scientists we should always be challenging and testing our assumptions and theories, not depending on them to tell us that we're right.