Week 3 Nap Lab: The coffee nap, tested on you

A within-subject comparison — plain nap against coffee nap — and a first encounter with how easily an experiment on yourself can fool you

8 March 2027

The lecture gave you a mechanism and a timetable. This lab asks whether you can detect the difference on yourself, and — more importantly — whether you should believe yourself when you say you can.

Before the lab#

• Keep your caffeine ordinary until the lab. If you normally have coffee at nine, have it at nine. We are adding one dose in a controlled position, not rebuilding your day.
• If caffeine is not part of your life, do not start for this lab. You are running the nap-only condition twice, which gives the class something it badly needs: a measure of how much the numbers move for no reason at all.
• Bring last week’s baseline. Without it this session produces two numbers and nothing to compare them to.

In the lab#

Condition A — plain nap. Baseline Karolinska and reaction-time median, 20 minutes lights-down, then both measures within two minutes of waking.

Condition B — coffee nap. Caffeine first, immediately before lights-down, then the same 20 minutes and the same two measures on waking. The order matters and it is the thing people get backwards: the dose goes in before the nap, so that it arrives as the nap ends.

Order is assigned, not chosen. Half the room runs A then B, half runs B then A. You will want to run the interesting one second. That instinct is exactly what the assignment exists to overrule, because a second measurement is not the same as a first one even when nothing else changed.

Pooling, twenty-five minutes. We put both conditions on the board and look at the spread before the means. The control group’s two plain naps will not give identical numbers, and the gap between them is the yardstick your A-to-B difference has to beat before it means anything.

Afterwards#

Both runs are Nap Log entries. Record the caffeine dose and the clock time, not just “coffee” — week 10 is about measurement quality and this is the week your own data either becomes usable or quietly doesn’t.

Write down one reason your personal result might be wrong. “Small sample” does not count; everyone’s sample is small and it explains nothing about your particular number. Expectation, time of day, and the order you were assigned are all better answers, and one of them is probably yours.