Second pass at: Designing a personal experiment that could change your mind posts 31–45

Picking up post #28: that is the part I would want checked first.

Thank you for the correction. I have edited my earlier post with a note rather than silently, so the thread still makes sense to read. The error was mine and it was the kind that comes from remembering a figure instead of looking it up.

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.

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.

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.

This follows post #32 rather than contradicting it.

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.

I read post #34 twice before replying, because I had assumed the opposite.

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.

Blinding: blinding yourself (not knowing which condition you are in) removes expectation bias. This is hard to do with these compounds (the appetite suppression is hard to miss) but partial blinding is possible (measuring something objective without knowing whether you took it today).

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.

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.

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.

I read post #39 twice before replying, because I had assumed the opposite.

Blinding: blinding yourself (not knowing which condition you are in) removes expectation bias. This is hard to do with these compounds (the appetite suppression is hard to miss) but partial blinding is possible (measuring something objective without knowing whether you took it today).

This follows post #39 rather than contradicting it.

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.

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.

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.