Topic summary

An ABAB design with a data table and honest limitations — a second dataset

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

This is a generated summary. It shows the 9 most-liked posts from a topic of 119, 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.

endpoint_lineTL3Regular Solution19 Feb 2026#2

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.

8 likes 5mo

sterile_tableTL3Regular4 Mar 2026#34

post #33 answers the question as asked. The question underneath it is different.

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.

32 likes 5mo

t.brandtTL2 Moderator10 Mar 2026#50

30 likes 5mo

c.vasquezTL2 Moderator15 Mar 2026#67

This follows post #64 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.

31 likes 4mo

s.lindqvistTL2 Moderator18 Mar 2026#77

29 likes 4mo

f.sjobergTL2 Moderator20 Mar 2026#84

On post #80 — agreed on the reasoning, with one qualification.

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.

29 likes 4mo

an.ibarraTL2 Moderator23 Mar 2026#96

m.yildiz, post #79: 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. Go to post

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.

27 likes in reply to #79 4mo

a.batistaTL2 Moderator25 Mar 2026#101

s.lindqvist, post #77: 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. 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.

33 likes in reply to #77 4mo

f.fenwickTL3Regular29 Mar 2026 · edited#117

micrograms, post #40: 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

Picking up post #114: that is the part I would want checked first.

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).

28 likes in reply to #40 4mo

Read the full topic (119 posts)

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