Designing a personal experiment that could change your mind — one year on posts 61–81

Confounding in personal experiments: other things change when you start a medication (season, exercise, diet, stress). Documenting those confounders helps you understand their contribution to the result.

I disagree with the reply above, and I think the disagreement is substantive rather than terminological.

The distinction being drawn does not survive when you look at the published data for this specific question. I would be glad to be shown wrong on this, because the version I am arguing against is more convenient.

On post #59 — agreed on the reasoning, with one qualification.

When to run an n-of-1: this design works when you want to know whether a treatment works for you, not whether it works in general. For that purpose, it is efficient.

Statistical analysis of n-of-1 data: comparing before versus after with a t-test or similar is one approach. Plotting the data visually is another. Both are valid.

Designing a personal experiment that could actually change your mind: that is the standard for an n-of-1 design. An experiment designed so that any result confirms what you already believed has not changed anything.

This follows post #63 rather than contradicting it.

Stopping rules: decide in advance when you will stop measuring (after a defined duration, after a defined number of measurements, or after a defined condition is met). Not deciding in advance means stopping when the result satisfies you, which is bias.

Washout periods: after stopping a medication, how long does it take for the effect to wash out? For compounds with a week-long half-life, roughly a month is needed to reach baseline. Using that washout period in a before-after design strengthens the inference.

Coming back to post #67, because the follow-up matters more than the original answer.

Sample size in n-of-1: you are the sample. Repeated measurements (weekly weighings, daily mood scores) increase the power to detect a real effect even though n=1.

Objective versus subjective measures: subjective measures (how you feel) are vulnerable to bias. Objective measures (weight, strength on a specific exercise) are less vulnerable but not immune.

I disagree with the reply above, and I think the disagreement is substantive rather than terminological.

The distinction being drawn does not survive when you look at the published data for this specific question. I would be glad to be shown wrong on this, because the version I am arguing against is more convenient.

Worth separating two things that post #68 runs together.

Statistical analysis of n-of-1 data: comparing before versus after with a t-test or similar is one approach. Plotting the data visually is another. Both are valid.

Confounding in personal experiments: other things change when you start a medication (season, exercise, diet, stress). Documenting those confounders helps you understand their contribution to the result.

Generalisability: a robust n-of-1 result applies to you. It does not tell you much about whether the effect generalises to others similar to you, much less to people different from you.