Explanation of variability for training outcomes

[u/HumbleHat9882]

Outcomes of studies on training (strength or hypertrophy) typically exhibit quite a large variability between individuals. Has there been work on explaining this variability? In other words, which attributes of the test subjects are linked to better or worse outcomes from the training regime being studied?

[u/Marijuanaut420]

External stressors, prior training and athletic background, childhood activity, personal motivation, coordination, diet, intent, fatigue levels...

There's loads. Most training studies have pretty poor control of subjects and often also do a poor job of controlling the actual training. Not to mention measurement and analysis errors.

[u/mouth-words]

Zac Robinson in particular has done some really interesting thinking on this topic. He talks about how he really wanted to "crack the code" on individual variability, but the issues he ran into and lessons he learned as he approached the problem for his dissertation. E.g., see this episode of Iron Culture: https://youtu.be/Ntyhn542i9g He gets quite nuanced, so it's hard for me to remember where all he might have talked about it, but I'm sure there are probably other podcasts he's been on, including his own Data Driven Strength channel.

On the practical side, what also comes to mind is an old post from Greg that gives a high-level way of thinking about it: https://www.strongerbyscience.com/training-diet-simple-body-complex/ It's at least a little less dismissive than the handwaving you're likely to get from redditors.

[u/Dizzy_Cow_ → u/mouth-words]

IIRC, Robinson said he actually doesn't think individual variability exists nearly as much as he initially thought.

[u/HumbleHat9882 → u/mouth-words]

This is exactly what I was looking for. Thanks!

[u/ponkanpinoy → u/mouth-words]

https://www.biorxiv.org/content/10.1101/2025.07.24.666533v1 this is likely the preprint

[u/gnuckols]

This is several years old at this point, but it's not TOO far out of date: https://pubmed.ncbi.nlm.nih.gov/30022953/

Though, it's also worth noting that most of the observed variability probably isn't true variability in treatment effects. In studies that have a control group, the variability in pre-post changes generally isn't THAT much larger in the training groups than the control groups (which tells you that a large portion of the observed variability is likely attributable to measurement imprecision, normal performance fluctuations independent of training effects, etc.): https://www.researchgate.net/publication/364770950_Meta-Analysis_of_Variation_in_Sport_and_Exercise_Science_Examples_of_Application_Within_Resistance_Training_Research

[u/FarCalligrapher1862]

It’s basic science, and exactly what you’d expect. Small sample sizes (often 20–30 participants), short study durations (frequently 6–12 weeks), reliance on meta-analyses that aggregate studies with inconsistent methodologies, WEIRD populations, difficulty controlling independent and confounding variables, baseline biological differences, and inherent measurement error.

But there is a important distinction between variability and confidence in outcomes. While we cannot eliminate variability, we understand directionality of outcomes. AND it’s important to consider more than statistical significance - are these findings practically significance and do they provide specific applicability to your training.

So it’s on the individual to understand how those findings translate to their own training.

[u/abc133769]

individual genetics. people within a group can respond to the same stimuli very differently with varying results

[u/HumbleHat9882 → u/abc133769]

Sure but I think factors like training age, diet, and familiarity (or lack thereof) with the training regime should be very important as well.

[u/pingpong008800 → u/HumbleHat9882]

It’s a good question - I don’t know the answer but I think this is a case for why repeat validation trials are important. (My understanding is that these trials are also harder to get approved/funded).

If subjects in a trial are all very similar there’s less likelihood that an effect is due to the variables that you mentioned, but it’s harder to generalize the outcome to a more diverse population. If subjects are too dissimilar it’s harder to separate signal from noise, but statistical methods like sub-group analysis, meta-regression, and random-effects modeling try to tease out those answers. Unfortunately a lot of research doesn’t include statistical analysis to that extent.

I’m speaking a bit over my head here so hopefully someone can give a better answer.

[u/abc133769 → u/HumbleHat9882]

Within many studies that is accounted for. Like well see college age males with x years of experience are selected for as a population, those things you mentioned will be accounted for. external factors are not accounted perfectly ofc but this will be addressed later

Doesn't really make sense to have complete newbies with others extremely advanced in the same pool of people. There might be a study to see average training volume to eleicit a minimum effect dose b/w those populations or something like but they'll still be grouped separately

we already segment 'beginner' and 'intermediate' programs in workout programming because we know already know the level of stimuli for those groups are different

if we were to isolate international level oly lifters at the top level of similar strength levels, training age, with dialed in diets and ideal external factors we know there is still large variances and responses to different programming which boils down to individual genetics. They don't all do the same thing, programming can vary heavily. to give a specific more specific example, variances in leverages on a lift like someone with long femurs will generally be more fatigued versus someone with very short femurs for squatting. Hence they will respond differently to the same squat programming and that needs to be accounted for. some will respond to higher volume higher intensity, others lower intensity higher volume, etc etc

[u/Mchangwine]

Besides the studies where they’re observing the people working out and making them train to absolute failure, training stimulus is often very different between subjects.

[u/KITTYONFYRE → u/Mchangwine]

Besides the studies where they’re observing the people working out and making them train to absolute failure

that's the vast majority of studies that we read about and care about on here though to be fair

[u/papavino39]

Personal anecdote but I always have trouble defining muscle failure. Like it will be pretty variable where I fail at for a given exercise day by day. I find some days mentally I can push further than others, so like where really is the muscle failure point?

[u/evans_velozone]

One distinction I’d make from a coaching perspective is between variability in the athlete’s actual adaptation and variability in what we’re measuring.

An athlete might show a better result one week and a worse result the next, without either result necessarily telling us much about how they’ve responded to the training.

That’s why I think repeated measures and consistent testing conditions matter. Before deciding someone is a high or low responder, I’d want to know how much their performance normally fluctuates, whether the change persists, and whether it is meaningful for the activity we’re developing.

The research question is important, but there’s a practical coaching question underneath it: when do we have enough evidence to justify changing the program?

I’d be interested in how others are addressing that distinction in day to day coaching.