Studies vs Experience in Training – Who to Trust?

When it comes to gauging the best techniques and programming for building strength and athleticism, there is sometimes a divide between the eidence and experience. What do you do when studies say one thing, but an experience coach says something else? Where does the truth lie?

Why We Need Studies

Looking at scientific studies is one of the very best ways to reliably test whether an idea or a theory works in practice. Indeed, that is what science really is: testing things.

This is why I take issue with people who say they “don’t trust science.” All this really means is that they “don’t trust testing things!”

Which, in my humble opinion, is not a smart way to go through life…

Without referencing studies, we would only be able to speculate what might be effective training based on guesses and anecdotal evidence. The latter is particularly dangerous: what we feel works for us (or a client) won’t necessarily apply to the general population. And of course, there may be confounding variables we are missing. What if the results they had were placebo, or even just coincidence? Scientific studies use statistics and other methods to ensure that results are at least likely to apply to a wide range of people.

How many times have you been given bad advice by a friend or a passer-by in the gym?

The Limitations of Studies

But with that said, scientific studies have their limitations too: especially when it comes to fitness and training.

First: not all studies are made equal. When reading and comparing studies, it is important to consider several factors. For instance: what is the sample size? And how was the sample obtained?

See also: How to Evaluate a Health Study

Do results of studies conducted on trained athletes necessarily apply to the general population? And vice versa? Do studies on men always apply to women? Many studies use something called “opportunity sampling” which simply means that they used the participants that were available to them at the time. This usually means university students! The better studies use a quota sample or stratified sample, designed to better reflect the demographics and ranges of the general population.

Of course, many studies will use animals–which is even more rife with issues!

Sample size is also important, although not as important as the overall “statistical significance” of the study. This tells you how likely the results are to have occurred purely by chance (not as a result of your intervention).

Confounding Variables

Most important of all is to control for the “confounding variables.” Confounding variables are any factors that could impact the results of your study and thereby skew your results. For example, if you gave one group of people a new supplement to increase strength (the “experimental group”), then compared their strength gains to a “control” group, you would hope to see the experimental group get stronger. But what if the experimental group were all training regularly, and the control group–by change–were couch potatoes? To prevent this confounding variable from having an effect, a good study like this needs to ensure that BOTH groups are performing the exact same training regime.

One of the most challenging confounding variables for researchers to deal with is the placebo effect. This is the known phenomenon wherein simply believing something will work often causes it to. Future chapters will shed light on why this may be the case! Another big issue is the bias of the researchers themselves, which is why the best experimental designs are “double blind.” This means that not even the researchers know which group is the control and which is the experimental group! That in turn prevents them from acting differently in any way, which could be picked up by the participants.

These are just two examples of confounding variables, but a good study will need to account for any number of them. What about protein intake? Or age and height? How about the time of day they train?

Then there are issues with the way some results are recorded. Is the study REALLY testing what you’re looking for?

Sometimes I wonder if study designers have ever actually lifted a weight in their lives. I remember one study concluding that pre-exhaust sets didn’t work for hypertrophy because there was less muscle activation in the target groups. Except activation and hypertrophy are NOT the same thing! The point is that the target muscle is fatigued and less able to perform the movement – of course other muscles will help out more. But at the same time, the work the muscle IS doing is now in a damaged state, potentially meaning increased blood pooling and microtears.

A Disconnect Between the Gym and the Lab

And this can then create problems in itself.

In real-life settings we don’t treat everyone equally. Everyone is different and responds to different approaches in training. A good strength coach will use “autoregulation” to adapt a training program to the person receiving it. They will provide advice and cues to help them make the exercise/intervention work for them.

We certainly wouldn’t take 30 untrained individuals and expect them all to start squatting the same weight with minimal preparation! Nor would we force people with different limb ratios and socket depths to squat with the precise same technique.

In short, a scientific setting can actually make an exercise less likely to be effective in many cases! Or it might simply miss strategies that could have been used to make it effective.

Even if we get a clean result and find that exercise X can result in a 10% improvement in Y, that doesn’t necessarily tell a functional coach whether said exercise is truly useful.

What if the exercise also creates a high risk of injury?

Or if it leaves the athlete too fatigued to do anything else?

What if combining two alternative methods could actually result in a 12% improvement?

The very fact that a controlled study MUST remove many confounding variables means that it is less relevant to a real-world athlete. Participants will be placed on unusual training protocols that may hurt their performance in other ways.

Observational Studies

We also need to be very careful with observational studies. These are studies that don’t involve an intervention, or laboratory settings, but which simply observe differences in the general population. While this can be very useful, it prevents us from controlling many variables. It also means that we can’t conclusively establish causality. That is to say that two things might co-occur, but that doesn’t necessarily mean that one caused the other. This is the OPPOSITE problem from the issue we just discussed with overly-controlled studies.

It may be that the type of coach/athlete to use exercise X is also the same type of coach/athlete to advise other effective training strategies that actually cause the results.

Do people who sit too close to the television wear glasses because the TV damaged their eyesight? Or do people with glasses have damaged eyesight, and therefore sit closer to the TV?

Conclusions

So, what do you do? My advice is to read as much as you can, both from studies AND from experienced coaches and writers. Then, with an understanding of how something is meant to work, take it into the gym yourself. The best people to learn from are the qualified coaches that ALSO have decades of experience under their belts.

Research is extremely important for pushing physical and mental training forward, but it’s also important to temper this with real-world experience and first-hand experimentation.

EVERYONE is different, so of course, what works for you, might not work “officially.”

And due to the very nature of science, we should never assume that training “rules” will never be overturned by future discoveries. Keep an open mind.

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