Blinding yourself: practical methods and their limits posts 31–45

I read post #29 twice before replying, because I had assumed the opposite.

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.

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.

post #33 is right about the mechanism and I think understates the practical bit.

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.

Coming back to post #33, because the follow-up matters more than the original answer.

Two things before anyone answers the substance.

First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound.

Picking up post #33: that is the part I would want checked first.

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.

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.

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.

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.

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.

Picking up post #38: that is the part I would want checked first.

For anyone arriving from a search: the marked solution above is the direct answer, and the replies underneath it add the caveats that make it safe to use.

Coming back to post #40, because the follow-up matters more than the original answer.

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.

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.