Physiology Friday #324: How Can We Make Science Useful?
It’s not the final answer, but it’s a starting point to inform practice.
Almost every time I read a new scientific study, and especially every time I sit down to write about one, I find myself returning to the same basic question:
What the heck can I actually do with this?
In the world of exercise science, nutrition science, and medical science, much of the work is ultimately pointed toward application. We study the body because we want to understand how it works. And we want to understand how it works because, at some point, we hope that understanding can help us live and perform better.
Maybe that means running faster (it does for me). Maybe it means eating in a way that better supports health. Maybe it means recovering more effectively, sleeping better, reducing disease risk, or making training decisions with a bit more confidence. Many of us come to science because we are looking for a better way to navigate the practical questions of being a human and athlete.
And yet, this is where things become complicated, because science rarely tells us exactly what will happen to us. Rather, it tells us what happened, on average, to a group of people under a particular set of conditions (this is a concept I touched on in last week’s newsletter).
I think this is one of the most important things to remember when we try to use science in real life.
Most studies report their findings as group averages. A group of people completes a training intervention, takes a supplement, changes their diet, follows a recovery protocol, or undergoes some other experimental condition. Then the researchers measure what happened and report the average response.
Beneath that average is usually a more complicated story. Some people improved a lot. Some improved a little. Some did not change at all. Some may have even moved in the opposite direction. But all of those individual responses are compressed into a single result. And that changes the type of meaning we need to place on any one study.
A group average is not a promise. It’s a signal that, in this population, under these conditions, using this intervention, the overall movement was in a particular direction.
It can tell us that there may be a mechanism worth understanding and maybe a protocol worth testing. It can give us a starting point.
I think this is where people sometimes become too pessimistic. If the population does not perfectly match us in age, sex, training status, health status, or background, then why should we care? If the result is only an average, and I am not an average, then what is the point?
These are fair concerns. But I don’t think these limitations are a reason to discard science.
All a study needs to be useful, in my opinion, is to tell us something about what might be possible, what conditions may matter, and what variables we should pay attention to.
A good study invites us to think differently. It invites us to ask whether a mechanism that showed up in one context might also matter in our own.
This is especially true in exercise science, where the same intervention can produce very different results depending on the person, the timing, and the surrounding context.
And science is incomplete without context. It always will be.
There is a similar issue with statistical significance, which often becomes another place where we flatten a study into something more absolute than it deserves.
A lot of people read a study and immediately ask whether the result was statistically significant. If it was, the finding is treated as real. If it was not, the finding is treated as if nothing happened.
Statistical significance is important. It helps us determine whether an observed effect is likely to reflect a real signal rather than random noise. But it is not the same thing as practical significance or biological plausibility.
Sometimes a study fails to reach statistical significance because the effect is genuinely small or inconsistent. Sometimes it fails because the sample size is tiny or the response highly variable.
We should not overstate findings from small or statistically uncertain studies, nor should we reduce every non-significant result to “this does not work.”
There is a middle ground that is more honest and (I think) more useful.
A study can be too small to prove something and still be interesting enough to inform future research or, more relevant to us as individuals, self experimentation, especially when the potential upside is meaningful and the downside is low.
This is how I often think about smaller studies, mechanistic studies, and early intervention trials. I do not read them as final answers. I read them as pieces of a larger model and do my best to tell you how we might put it into practice.
Maybe this is not something I would tell everyone to do, but it is something I would pay attention to, or something I might test under the right conditions.
If we waited for complete certainty before changing anything, we would almost never change anything. So instead, we make decisions under uncertainty.
It shows us what happened when other people tried something under controlled conditions. It helps protect us from relying only on anecdotes, intuition, marketing, or whatever happens to be popular at the moment (highly applicable today).
At some point, the question becomes personal and practical. Is this relevant to me? Is this relevant to my goals? Is the potential benefit worth the cost, time, risk, or inconvenience?
And perhaps more importantly…
What would count as evidence that it is helping? What would count as evidence that it is not?
If a study suggests that a certain training strategy improves endurance performance, that does not mean it will automatically improve your endurance performance. But it may give you a reasonable starting point. You can try it, pay attention to the response, and interpret your own experience in light of the broader evidence.
The same applies to supplements, diet changes, sleep strategies, or recovery tools. The study gives you the idea. Your response helps determine whether the idea belongs in your life.
Let me be clear that this is not the same as saying that personal experience overrides scientific evidence. It does not. A single anecdote cannot overturn a body of research. But once you move from population-level evidence to individual practice, your individual response matters.
This is why I think the right response to variability is not to dismiss science, but to become more thoughtful in how we apply it.
When I read a study, I try not to ask only whether it “worked.” I try to ask what kind of signal it provides.
Those questions are often less satisfying than a simple yes or no. But they are usually closer to the truth.
They also reflect how science itself works. It’s not a static collection of final answers. It is a process of revision. That can be frustrating if what we want from science is certainty. But I think it is reassuring if what we want is progress.
When it comes to human health and performance, our model will almost always include the phrase “it depends.”
But “it depends” is often the beginning of a better question. Science helps us understand the conditions under which something may or may not work.
That is also what I hope this newsletter does each week!
I do not want to simply take a study and turn it into a headline or pretend that every paper gives us a clean answer.
Most of us are not trying to live as the average participant in a study. We are trying to make better decisions in our own lives. That happens by noticing patterns, testing ideas, updating our beliefs, and slowly becoming a little less wrong about how the body works and how we might take better care of it.
Thanks for reading. See you next Friday.
~Brady~