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Posted by on Feb 11, 2016 in Health, Training

N-of-1: The Power & Importance of Self-Experimentation

N-of-1: The Power & Importance of Self-Experimentation


We live in a culture and world ruled by science. Logical reasoning and thinking is the gold standard for anyone who wants to be taken seriously. We’re told what the science says, and we’re expected to believe it and incorporate it into our lives. And while science is definitely one of the best tools that we have for exploring our collective experience and determining what path we should take, many times what the science says, and what works for us as individuals, are two very different things.

The only way to truly explore our own experience and determine our own best path to take (whether it is lifestyle, diet, exercise, etc…) is to become our own scientist and test subject. Hence the N-of-1 study. In science, the N is the number of participants in studies, and usually the higher the number is, the more “power” the study can have. But the vast majority of the time, none of the N’s are you.


The Pitfalls of Mainstream Science

The biggest problem with scientific studies is that they don’t include us. And as each and every one of us has a different makeup in mind and body, what works (or doesn’t) for someone else, or for a whole group of people, may have no bearing on you. Even if a study comes out showing that this new awesome diet, training program, lifestyle habit, etc… works for 95% of people, you may be in the 5% that it doesn’t work for. Just the same, almost all terminal cancers with a survival rate of “less than whatever percent”, can mean nothing to the one-out-of-a-thousand-person who managed to survive it.

Science is also only as good as the tools that it has at its disposal. If we haven’t developed a tool yet to measure a certain phenomenon, that doesn’t mean that that phenomenon doesn’t exist. This is a major pitfall that the scientific community runs into. And while in our own N-of-1 scientific studies, we’ll still be constrained by the tools that we use, we have the added benefit of being able to measure changes in the way that we feel and think- both valuable tools that, due to subjectivity, are underused in traditional scientific studies.

The next problem with trusting large scientific studies is that we’re putting our faith in the researchers that came to the correct conclusion based on the data that they acquired (or that there is a definite conclusion to come to). Data and statistics by themselves mean nothing; they only develop meaning once we assign it to them. And you could give the same data to 5 different experts, and each could come to a different conclusion. Many times people can come to completely opposite conclusions given the same exact data set.

This brings us to the final major issue with following mainstream science. Science is run by human beings, who, based on desires, ideologies, and interest, are inherently biased. Every study requires a substantial amount of money to run, so where’s that money coming from? What conclusion would best benefit the people financing, and the people running, the study? Maybe one result could mean millions, or billions, of dollars for a company in sales of a product, while the opposite result leaves that same company at a huge loss of money and time. Maybe one conclusion leads a researcher to be able to publish a book, or win an award, or gain tenure, while the opposite conclusion leads to none of those things.

And while we certainly will have our own set of biases when performing studies on ourselves, the desire will almost always be for us to achieve a positive outcome. And if it does indeed influence our results, having a positive-outcome bias will oftentimes lead to positive results. And at the end of the day, isn’t that what it’s all about? Most people would gladly through objectivity and validity to the wayside if it meant a positive result or mental state.

So we just pointed out some flaws in trusting mainstream science, and we saw how the results and conclusions that researchers come to may or may not have any application to your own life. That being said, the Scientific Method itself is a brilliant tool that we can apply to our own lives to determine the best path to take to get our desired results.  So let’s explore this idea, and look at how you can use the scientific method to get the most out of your own N-of-1 study.


Applying the Scientific Method to Self-Experimentation

Below are the six major steps of the scientific method. Let’s look at each and how they relate to self-experimentation.




Ask a Question

First, we’ll start we’re all science starts, with a question. As we continually observe our own lives- Our training, our diet, our lifestyle, our energy levels, our performance, etc… we’ll invariably come up with questions. How could I do this better? Why do I have pain with this? How can I perform better at that? Why do I feel this way all the time? What’s the best way to accomplish this goal?

These questions can be specific- What can I do to add 5” to my vertical jump in the next 6 weeks? How can I increase my serve speed by 10 mph in the next 3 months? Or they can be broad- What’s the best diet for me to put on muscle? How can I change my sleep so that I can wake feeling rested and ready to perform my best?

When developing our question, we must remember that the more specific the question, the more specific the results will be. If we test a very broad question (which makes it difficult to control) we will have a big answer, but we won’t be able to be entirely sure if what we tested was the definitive cause of our result.


Do Background Research

So now that we have our question, we need to do some research to look at possible answers and possible ways that we can test it. This is the phase of our own self-experimentation in which existing science and research is very valuable to us. While we don’t know if what the science says is true for us, it gives us at least the knowledge of what has worked for people in the past and where we may start with developing our own hypothesis. We can consult the internet, scientific journals, books, experts in the area, someone who’s had success (or not) with a certain program.

If we want to increase our vertical jump by 5” in the next six weeks, it would be good to look at ways that people have done it in the past. This gives us a basis to develop our hypothesis and our method of testing that hypothesis. Researching plyometrics, squat variations, and resisted jumping programs will give us a background on what people have successfully used in the past to increase their vertical. Keep in mind that more research doesn’t necessarily mean better results.


Construct a Hypothesis

Now that we have our question and have done some research, it’s time to come up with a hypothesis. This is where we get to either go with what has worked for others, or what the science says will work, or we can come up with our own idea that we want to test. It’s often safer to base our hypothesis/experiment on something that’s been done before. But, the cool thing about self-experimentation is that you can try all kinds of different things and come up with your own original hypothesis. Maybe you just have that gut feeling that something will work. Great, give it a try.

So come up with an idea: I think that intermittent fasting will be the best way to lose 10lbs of fat in a month without losing muscle mass. Or, I think that performing a resistance-based jumping program 4x/week for six weeks will increase my vertical jump by 5”.


Test Your Hypothesis by Doing an Experiment

So far, all of the previous steps have been things that we naturally tend to do when trying something out anyways. We have a question, ask people what they think, and then come to a conclusion. Yes they are part of the scientific method, but they’re also just common sense (probably the same way our early ancestors determined whether something was edible or not). So this is where the science comes in. It’s time to test our hypothesis.

We first need to come up with baseline measures. Whether that’s a performance measure (1-rep max squat, vertical jump height, etc…), or how we feel (I wake up feeling exhausted, or every time I do a long run, or heavy deadlifting, I have pain in my low back). The more specific our baseline measures are, the better. If you’re testing something subjective (pain, energy levels, etc) create some sort of scale (0-10 is always good) and measure it that way.

Depending on what you’re testing, and what resources you have, you can get really high-tech and have various lab tests performed (blood, urine, fecal, etc) or advanced performance measures (VO2 max, EMG, etc) to create more of an objective baseline. The best approach is to have at least one or two objective measures, but then to also have some subjective measures on how you feel. If our joint ROM increased significantly, but our pain or perceived tightness stayed the same, then the intervention might not have been successful.

Once the baseline measures are taken, begin the experiment. It should have a start and an end date. We have to be consistent and disciplined with sticking to the program for the experiment to have any validity. And we should attempt as much as possible to keep everything else in our training, lifestyle, and diet as consistent as possible (controlled) so that we’re only changing the thing that we’re testing.

We can remeasure our baseline(s) throughout the experiment, or at the end, but it’s a good idea to keep a journal of our subjective measure(s) throughout the whole experiment. Test, intervention, retest. At the end of the study, we should have some good data to look at.


Analyze Your Data and Draw a Conclusion

Once we have our data, we need to draw conclusions. For most self-experimentation, this is fairly straight forward. Do you feel better? Did you reach your goal for increasing a certain performance measure? If you need help in interpreting your data, it’s always good to talk with experts (coaches, trainers, doctors, academics) and get their take on it, especially if you’re using lab tests or other high-tech measuring tools.

When coming to our own conclusions, it’s important to have some sort of objectivity. While we almost always want our hypothesis to be true, sometimes we learn more from it being wrong. And it’s ok if it was wrong, or if we didn’t get the results that we were hoping for. Part of being a true scientist is looking at the data and being able to come to an objective conclusion, and use that for future research.


Communicate Your Results

This final step is arguably the least important in the self-experimentation process. Our natural tendency is to communicate with the tribe the findings of our experiment to help protect them from harm (if things didn’t go well), or to help them to be stronger/healthier (if it did work for us). But, our entire argument for doing self-science is that we want something that will work for us specifically. If we start telling everyone what to do based on our results, we’re defeating the whole idea of it. At the same time, it is ok to tell others what we did and what we found, and let them make the determination about how it may, or may not, apply to them.


So Now What?

So once we’ve done the experiment, gotten our results, and came to some sort of conclusion, what do we do next? Maybe our hypothesis wasn’t right and didn’t help us. Or maybe it did, but not to the level that we were hoping for. How we apply what we learned in our experiment is just as important, if not more so, as whether the intervention worked or not.

If what we did worked perfectly and we’re at our ideal goal, then we should move on and explore a different area that we can get better in. If we got good results, but maybe not quite as good as what we were hoping for, we can tweak our program/intervention and test it again, or we can scrap it completely and try something else. If our experiment was a complete disaster, then we come up with another way to attack the problem and keep running experiments until we find something that works.

At the end of the day, self-optimization will always be about persistence more than it is about getting things right the first time. And no matter how optimized we get, there’s always room for improvement. That extra 1% (which often is the hardest to get) is many times the difference between being a world champion and coming in second, or more importantly, the difference between becoming your best self, and falling short of your potential.

Mainstream science is not the enemy. It gives us all a map of where to begin with our own experimentation.  But in order to maximize our own experience, we need to become masters of running our own experiments. Never stop trying new things, never stop exploring your own world, and never stop progressing towards a better you.



  • Mainstream science has a number of flaws including:
    • Studies aren’t being done on you personally and therefore may not be applicable to you as a unique individual
    • Data/Statistics are open to interpretation so the message that we receive is only one interpretation of a set of results
    • Scientific conclusions can be, consciously or unconsciously, fraught with biases and
  • Self-Experimentation is the gold standard of figuring out what works (and what doesn’t) for you.
  • Applying the Scientific Method to your own self-experimentation is the best way to determine whether or not something works for you.



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