The hardest part of experimenting with health is not execution.
It is interpretation.
It is surprisingly easy to believe something is working when you want it to work. Expectation, effort, and hope all bias perception. Without structure, a wellness experiment quickly turns into confirmation rather than discovery.
This post explains how I design wellness experiments to reduce self deception and keep conclusions grounded.
Step One: Define the Question Clearly
Every experiment starts with a specific question. Not a hope. Not a promise.
Bad questions sound like:
“Will this make me healthier?”
“Is this good for me?”
Useful questions are narrower:
“What changes if I introduce this intervention?”
“Which biomarkers or metrics might plausibly shift?”
“What would count as no effect?”
A clear question limits how much interpretation can drift later.
Step Two: Decide What Will Be Measured Before Starting
Measurement comes before action.
Before I introduce any intervention, I decide:
• Which biomarkers or metrics matter
• How often they will be measured
• How long the experiment will run
If measurement is decided after the fact, it is too easy to chase whatever looks favorable.
Precommitting to metrics creates discipline and protects against selective attention.
Step Three: Establish a Baseline
Without a baseline, change cannot be interpreted.
I collect enough pre intervention data to understand normal variability. This includes lab values, wearable metrics, and subjective notes.
The goal is not perfection.
The goal is context.
Baseline data reveals how much fluctuation exists even when nothing is changed.
Step Four: Change One Variable at a Time
This rule is simple and often ignored.
If multiple things change at once, attribution becomes impossible. Supplements, diet, training, sleep, and stress all interact. Introducing more than one variable guarantees confusion.
One change does not guarantee clarity.
Multiple changes guarantee the absence of it.
Step Five: Track Confounders Honestly
Life does not pause for experiments.
Sleep, travel, stress, illness, workload, and social changes all influence outcomes. I document these variables not to control them perfectly, but to acknowledge their influence.
Ignoring confounders does not remove them.
It only hides them.
Step Six: Separate Feeling Better From Measured Change
Feeling better matters.
It is just not sufficient.
I track subjective experience, but I treat it as one signal among many. Mood, energy, and motivation are influenced by expectation and context.
Data does not invalidate feelings.
It grounds them.
When subjective improvement is not supported by objective change, I note the discrepancy rather than forcing alignment.
Step Seven: Commit to Conservative Interpretation
The easiest mistake is overinterpretation.
A single improved marker does not mean an intervention worked. A short term change does not imply durability. Correlation does not equal causation.
I deliberately understate conclusions and resist narrative closure. Ambiguity is not failure. It is honesty.
Step Eight: Document Everything, Including Mistakes
This project is not about appearing optimized.
When I miss measurements, change variables unintentionally, or misjudge timing, I document it. These details matter because they shape interpretation.
Clean stories are often incomplete stories.
Step Nine: Decide in Advance What Would Change My Mind
Before starting, I define what would count as:
• Meaningful improvement
• No meaningful effect
• A reason to stop
This prevents goalposts from moving after results are visible.
An experiment without stopping criteria is not an experiment. It is a belief.
Why This Approach Matters
Wellness experimentation is vulnerable to self deception because outcomes are often subtle and slow.
Structure creates friction.
Friction protects truth.
This method does not guarantee clarity. What it does is reduce the risk of fooling myself while increasing the chance that whatever I learn is actually useful.
That is the goal of MyBioHackJourney.
Not to prove something works.
But to document what happens when you measure carefully and stay honest.