Why most of what we think we know about biology is wrong
We all know the mass media relies entirely on sensationalism to drive revenues, and the rational conclusion is therefore that the mass media cannot be trusted as a source of information.
While occasionally the facts may be correct, the intentional lack of appropriate context renders even facts misleading. Most of the time we’re simply unaware of this phenomenon and so we go from one scare story to the next. We live in a world of instant fear and near-instantaneous forgetting. Yesterday’s big story is eclipsed by today’s sensation; the past is pasted over with today’s transient screaming headline.
While the political results are disastrous, our understanding of the world around us in general is equally damaged. The mass media, when not endlessly creating fear over whatever happens to be the sensation du jour, loves to report “scientific” findings. Only, of course, what’s reported is as far from the truth as it’s possible to get while avoiding mention of fairies and goblins.
To see why this is, let’s perform a thought experiment.
Let’s imagine that for fifteen years a team of researchers has been investigating a strain of fungus that seems to have some inhibitory effect on one aspect of the mechanisms by which animal cells manage the process of cell division. To do this, the team cultured a specific cell (maybe from the lining of the animal’s cheek, maybe from its liver) that can be kept alive in a petri dish and which will, under the right conditions, divide every few days. This is a pretty artificial setup and the cell was chosen precisely because it can survive in such a setup.
Now let’s imagine that eventually the research team identifies a protein produced by the fungus that reduces the speed of cell division in the petri dish by increasing the time required for division from 72 hours to 76 hours. The team then diligently writes up its results and eventually the paper is published. At the very end of the paper there’s a brief speculative section that notes how most cancers arise because of rapid uncontrolled cell division and therefore asks whether the fungal protein the team has identified could, perhaps, one distant day in the future, potentially point the way toward some sort of druggable molecule for certain types of cancer upon which this protein might have some slight inhibitory effect.
Now, an eager young assistant staffer working for a relatively obscure online publication happens to see this, perhaps because they’re dating someone who had to read the paper as part of their own research initiative. The journalist doesn’t understand anything about cancer, biology, or science in general but “researchers discover cure for cancer!” is a great headline and will appeal to their editor because there hasn’t been an airplane crash or a school shooting or a child kidnap for three whole days and the publication needs something to grab eyeballs for a few brief moments.
Naturally, other journalists (who spend their days reading what other journalists are covering, in order to know what they should cover themselves) pick up the story. Before we know it, this new cure for cancer has been spread by the world’s media outlets and ordinary people start asking their doctors why they aren’t being told about this new discovery.
Of all the millions who hear about this new miracle cure for cancer, not one in a million will stir themselves to dig up the actual paper on which all the hype has been based. And so not one in a million will discover that the story was complete nonsense.
Worse still, most people are entirely unaware of the technical jargon that scientists inadvisably use, and this leads to even greater errors of comprehension.
Sadly, all craft guilds invent jargon by means of which to draw a line between “insiders” and “outsiders.” It’s not only a social signaling mechanism but also protective; if outsiders don’t understand what’s been said, they’re less likely to spot mistakes and falsehoods and thus the jargon-users will be able to get away with all manner of sloppy behaviors and unfounded assertions. That’s why doctors and lawyers (today’s most obvious craft guilds) use jargon. Scientists are no less guilty of this habit than the craft guilds, and inevitably it leads to confusion.
We’ll now look at one common source of confusion: the word heritability.
Heritability is a term used in biology for a very specific purpose. Most people outside biology departments imagine that heritability is equivalent to inheritance. Thus if a report states that 60% of variation in the height of corn plants is heritable, the average person will imagine this means that 60% of the variation in height is caused by the plant’s genes, with the remainder presumably coming from soil conditions, weather, and suchlike.
In fact, this is not at all what heritability means. Heritability is about variance.
To illustrate what biologists mean when they use the word heritability, let’s take a couple of simple examples, both courtesy of Stanford’s Professor of Behavioral Biology, Dr Robert Sapolsky.
Let’s imagine we’re looking at the human hand. We have two hands, and the default condition is that each hand has four fingers and one thumb. Thus, if we count the thumb as a finger (which it is) then the default for humans is to have ten fingers. But some people lose fingers through accidents. If we took a random sample of ten million people, we’d end up with fewer than one hundred million fingers.
From the perspective of a biologist, the number of fingers is not heritable. There is no gene or set of genes that predicts the variation in the number of fingers we see in the real world.
Let’s take another example. We travel in time back to 1952, a period of great conformity in the USA. We notice that nearly every female over the age of 18 is wearing ear-rings and no male of any age is wearing ear-rings. By simply knowing the gender of an anonymous subject and, if they’re female, their age, we can to a very high degree of precision predict whether or not they will be wearing ear-rings. From the point of view of a biologist, this trait (the wearing of ear-rings) is strongly heritable. If you have no XY chromosome we can predict you will be wearing ear-rings.
It’s easy to see how ordinary people can get precisely the wrong picture when they read or hear that a particular trait is heritable.
Things are even worse than this, however.
One of the great mantras of science is that it’s important to study individual variables in order to establish cause-and-effect. This notion runs more or less all the way back to Rene Descartes, who thought that living organisms were akin to clockwork time-pieces. His core insight, reductionism, basically said “when you see something complex, take it apart until you’ve reached the smallest divisible unit. When you understand the small components, you will understand the whole machine.”
Unfortunately Descartes failed to realize that that’s not at all how complex systems work. You can’t understand a tsunami by studying the quarks inside a proton. You can’t understand the social behavior of an ant colony by studying the neurotransmitter than causes an ant’s leg muscle to twitch. When we disassemble complex systems we often get further and further away from understanding them.
Unfortunately, the concept of emergent properties (e.g. those things that become evident only at a specific level of complexity) is still not widely understood across the scientific community. Thus, grants committees still far too often insist on experimental designs that ensure a completely incorrect way to study a problem.
Let’s illustrate this by way of another example.
Let’s imagine we have a species of grass in which there are three predominant proteins that have a significant influence on the growth of the plant. We can call these proteins p1, p2, and p3. Individual plants will tend to have one dominant form of the protein, with the other two forms appearing in much reduced amounts. Let’s say we want to understand the influence of these three proteins on the growth of the plant.
The “gold standard” will be to remove every possible variable except the three proteins we want to study. So we’ll take our plant samples in sufficient numbers for statistically valid results and ensure they all have precisely the same environment: same soil, same amount of light and water, same temperature, and so forth. Then we’ll measure the plants daily for some period of time and see what we get.
Let’s imagine that the 100 plants with mostly p1 reach a height of 2 meters, the 100 plants with mostly p2 reach a height of 1.5 meters, and the 100 plants with mostly p3 reach a height of 1 meter.
Hooray! We’ve definitely got a result we can write up, with a statistical confidence level of >p95. We’ve proven that if we can somehow get enough p1 into this plant species, we’ll increase the average height attained all around the world. Good news for farmers everywhere!
Except… let’s imagine this particular species of grass grows all around the world. We pulled our samples from the warm moist environment of North Carolina and we replicated that environment in our laboratory. Another researcher, working on the arid plains of Kazakhstan, has just written up their own version of our experiment. Strangely, their results showed the precise opposite of ours! They found that plants with mostly p3 reached a height of 75cm, plants with mostly p2 reached a height of 45cm, and plants with mostly p1 reached a height of only 25cm.
What’s going on? Is someone cheating? Did someone’s experimental design contain a huge flaw?
What happened was simple: by removing all variables with regards to environment, the experimenters created a highly limited scenario that couldn’t reveal the interactions between plant proteins and environments.
All living creatures interact with their environment. Our genes enable us to respond to exogenous factors. By removing environment as a variable, in accordance with strict Cartesian principles, we rendered null the value of the experiment we were conducting. This happens far more often in biology than is commonly understood (especially by grants committees).
In our example here, what we find is that while p3 may not be optimal for the plant when there’s plenty of warmth and moisture available, it’s a good protein to have in reserve in case things dry out. Likewise, while p3 may be optimal when things are arid, it’s good to have some p1 in reserve in case there’s sudden rainfall.
Yet the “gold standard” of experimental design would, in both the USA and in Kazakhstan, actually ensure we’d never find out this important fact.
The take-away is simple: biology is highly complex. There are no easy answers, and most definitely there are no simple answers.
So the next time you read or hear a story about how “X cures cancer!” or “Y amino acid makes you smarter!” or “eating Z is bad for you!” just remember that the chances are it’s bogus. The journalist writing the story almost certainly simply copied it from another journalistic source and has zero understanding of the actual science involved. The editor removed any allusion to subtlety and potential caveats, knowing that ordinary people want simplistic stories, not difficult-to-understand nuances. The academic paper itself may have been deeply flawed, with either insufficiently large sample sizes, poor statistical analysis, or incorrect experimental design.
This isn’t to say that science is bogus or that no result is true. Science is the only approach ever invented by humans that does, eventually, lead to deeper understanding of how things really work. In the end, regardless of personal authority or neatness of story, facts come to dominate (though it may take a very long time indeed for this to occur). Science is the only path we have to the truth; everything else is worthless.
But science is complicated because reality is complicated. The mass media is all about distraction, fear-mongering sensationalism, and entertainment. It is thus the least reliable source imaginable (aside from right-wing shock-jocks) for anything remotely resembling information.
If you want to know about something, the Internet has made a wide range of highly credible sources available at the click of a button. Seek out online free courses from Stanford, MIT, and other top-rank universities. Ignore the sensationalist mass media and ignore lone doctors who make absurd proclamations in best-sellers about gluten, vaccinations, or aliens from space who’ve come to perform anal probes.
Reality is complex.
Therefore, any simplistic story is 100% guaranteed to be wrong.