Topic summary

Coming back to: Why most self-reports here are not experiments, and that is fine

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

This is a generated summary. It shows the 9 most-liked posts from a topic of 63, 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.

t.kulkarniTL3Regular Solution7 Sep 2025#4

post #2 answers the question as asked. The question underneath it is different.

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.

7 likes 11mo

BDraganovTL2Member28 Sep 2025 · edited#8

t.kulkarni, post #4: post #2 answers the question as asked. The question underneath it is different. 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. Go to post

post #7 is right about the mechanism and I think understates the practical bit.

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.

25 likes in reply to #4 10mo

p.krastevTL2 Moderator2 Oct 2025 · edited#9

Coming back to post #7, because the follow-up matters more than the original answer.

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.

24 likes 10mo

a.amankwahTL2 Moderator10 Nov 2025#18

p.krastev, post #9: Coming back to post #7, because the follow-up matters more than the original answer. 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. Go to post

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.

32 likes in reply to #9 9mo

p.krastevTL2 Moderator10 Dec 2025#26

This follows post #23 rather than contradicting it.

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.

29 likes 8mo

c.ramosTL2 Moderator7 Jan 2026#34

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.

31 likes 7mo

methods_marginTL3Regular3 Feb 2026#42

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.

24 likes 6mo

g.pemberton_ukTL3Regional · UK15 Feb 2026#46

post #45 answers the question as asked. The question underneath it is different.

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.

32 likes 5mo

f.haddadTL2 Moderator13 Mar 2026#54

Worth separating two things that post #50 runs together.

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.

25 likes 5mo

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