Topic summary

Second pass at: Designing a personal experiment that could change your mind

Research Methods › N-of-1 designs worked example effect size confounding data table Solved

This is a generated summary. It shows the 6 most-liked posts from a topic of 45, in their original order, with the accepted answer included where one exists. It is a reading aid and it will miss nuance — the full topic is the record.

sterile_tableTL3Regular16 May 2025#3

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

Practical note that does not fit anywhere else. Whatever you conclude from this topic, write down what you did and when. The single most useful thing in your own records is not any individual result; it is that they are dated and consecutive.

29 likes 14mo

g.bakkenTL2 Moderator Solution16 May 2025#4

Picking up post #2: 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.

14 likes 14mo

s.kuuselaTL2 Moderator17 May 2025#13

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.

27 likes 14mo

p.novotnyTL2Regular19 May 2025#24

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.

30 likes 14mo

e.verhoevenTL2 Moderator20 May 2025#32

32 likes 14mo

OstrowskiTL2Member21 May 2025#39

j.ivaturi, post #2: 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. Go to post

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.

30 likes in reply to #2 14mo

Read the full topic (45 posts)

This topic was referenced in

Coming back to: Why most self-reports here are not experiments, and that is fineResearch Methods › N-of-1 designs · 62 replies
Pre-registering a personal experiment, seriously — a second datasetResearch Methods › N-of-1 designs · 66 replies

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