How to Run Sleep Experiments on Yourself

You've tracked your sleep. You've found correlations. You have hypotheses about what affects your sleep.

Now it's time to test them.

Running experiments on yourself—sometimes called n-of-1 experiments—is the most powerful way to move from "this might affect my sleep" to "this definitely affects my sleep."

Why Experiments Matter

Observational data has limits. You might notice that late caffeine correlates with poor sleep. But:

• Maybe late caffeine happens on stressful days
• Maybe stress causes both late caffeine and poor sleep
• Maybe caffeine isn't actually the cause

Experiments isolate variables. When you change one thing and control everything else, you can be much more confident about cause and effect.

Key Insight: Observations reveal correlations. Experiments reveal causation. You need both.

The Basic Experiment Structure

Step 1: Form a Hypothesis

Be specific about what you're testing:

Weak hypothesis: "Caffeine affects my sleep" Strong hypothesis: "Caffeine after 3pm reduces my sleep quality by at least 1 point on average"

The stronger hypothesis is testable and measurable.

Step 2: Define the Experimental and Control Conditions

Experimental condition: What you're testing Control condition: Your baseline comparison

Example:

• Experimental: No caffeine after 12pm
• Control: Normal caffeine routine (cutoff around 4pm)

Step 3: Decide on Duration

How long to test each condition?

Minimum: 5-7 days per condition Better: 7-14 days per condition Why: Reduces day-to-day noise and captures weekly patterns

Step 4: Control Other Variables

The key to good experiments: change only one thing.

Keep constant:

• Sleep opportunity (bedtime)
• Other inputs (alcohol, exercise, screens)
• Sleep environment
• Wake time

If other variables change during the experiment, your results become harder to interpret.

Step 5: Track Everything

Continue logging inputs and sleep quality throughout the experiment.

Step 6: Compare and Conclude

Did the experimental condition produce different outcomes than control?

Example Experiment: Caffeine Cutoff

Hypothesis: Cutting caffeine by 12pm will improve my sleep quality by at least 1 point.

Week 1-2 (Control):

• Normal caffeine routine (cutoff around 4pm)
• Track sleep quality daily

Week 3-4 (Experimental):

• No caffeine after 12pm
• Track sleep quality daily
• Keep all other inputs the same

Results:

• Control average: 6.2 sleep quality
• Experimental average: 7.1 sleep quality
• Difference: +0.9 points

Conclusion: Early caffeine cutoff improves sleep quality by almost 1 point. The hypothesis is supported.

Common Experiments to Run

Bedtime Timing

Hypothesis: Earlier sleep opportunity improves sleep quality. Test: Two weeks at current bedtime vs. two weeks 30-60 minutes earlier.

Weekend Consistency

Hypothesis: Maintaining consistent timing on weekends improves Monday sleep. Test: Two weeks with weekend drift vs. two weeks with consistent timing.

Caffeine Cutoff

Hypothesis: Earlier caffeine cutoff improves sleep. Test: Two weeks with normal cutoff vs. two weeks with cutoff 2 hours earlier.

Screen Time

Hypothesis: No screens before bed improves sleep. Test: Two weeks normal screen use vs. two weeks screen-free hour before bed.

Exercise Timing

Hypothesis: Morning exercise improves sleep more than evening exercise. Test: Two weeks of morning exercise vs. two weeks of evening exercise.

Alcohol

Hypothesis: No alcohol improves sleep quality. Test: Two weeks with alcohol vs. two weeks without.

Designing Good Experiments

Keep It Simple

Test one variable at a time. Changing multiple things makes results impossible to interpret.

Bad experiment: Switch to earlier bedtime AND cut caffeine AND stop alcohol Good experiment: Just switch to earlier bedtime, control everything else

Make It Long Enough

Short experiments are noisy. A few good or bad nights can skew results. Aim for at least a week per condition, preferably two.

Be Strict About Control

If you're testing caffeine cutoff, actually maintain the cutoff. One slip confounds your data.

Account for Weekly Patterns

Sleep quality often varies by day of week. Make sure both conditions include full weeks, or patterns may skew results.

Document Everything

Note anything unusual that might affect results—stressful events, illness, travel. You may need to exclude those days from analysis.

Interpreting Results

Clear Positive Result

Experimental condition clearly better than control (by your pre-defined threshold).

Action: Implement the change permanently.

Clear Negative Result

Experimental condition clearly worse than control.

Action: Don't make that change. Try something else.

No Clear Difference

Conditions are similar.

Action: Either the variable doesn't affect you much, or the experiment needs to be longer/more controlled.

Mixed Results

Some metrics improved, others worsened.

Action: Evaluate trade-offs. Is the improvement worth the cost?

The A/B/A Design

A more rigorous design:

Period A: Control (baseline) Period B: Experimental (test) Period A: Control (return to baseline)

If quality improves in B and returns to baseline in the second A, you have stronger evidence that the change caused the improvement.

Practical Tips

Start With Your Strongest Hypothesis

Test the correlations that look most promising first. Quick wins build confidence.

Don't Experiment During Unusual Times

Vacation, illness, major stress—these aren't good times to run controlled experiments.

Be Patient

Good experiments take time. Rushing leads to inconclusive results.

Accept That Some Experiments Will Fail

Not every hypothesis will be confirmed. Failed experiments are still valuable—they tell you what doesn't matter.

Track the Experimental Period

Note which days were control vs. experimental in your tracking. You'll need this for analysis.

Common Mistakes

Mistake 1: Changing Multiple Variables

"I'll try earlier bedtime AND less caffeine." Now you don't know what helped.

Mistake 2: Too Short Duration

Three nights isn't enough. Day-to-day variation will overwhelm any real effect.

Mistake 3: Inconsistent Control

"I mostly avoided caffeine after 2pm." Mostly isn't controlled. Be strict.

Mistake 4: Biased Interpretation

You want the experiment to work, so you interpret marginal results as success. Set success criteria before starting.

What to Track in Trendwell

Element	What to Track	Why
Phase marker	Control/Experimental	Know which condition each day was
Target variable	The thing you're testing	Direct measure of experiment
Sleep quality	Primary outcome	What you're trying to improve
Other inputs	Everything else	Confirm they stayed controlled

Next Steps

• Read: Understanding Sleep Correlations
• Read: Establishing Your Sleep Baseline
• Read: What You Learn After 30 Days of Tracking
• Start tracking: Get started with Trendwell

You are your own best research subject. The sleep advice that works is the advice your own data validates. Run the experiments, trust the results, and optimize for yourself.

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Last updated: January 2026