Topic summary

Blinding yourself: practical methods and their limits

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

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.

t.dumitruTL2 Moderator19 Feb 2026#6

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).

24 likes 5mo

KTurkingtonTL3Regular11 Mar 2026 · edited#10

I read post #8 twice before replying, because I had assumed the opposite.

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 5mo

s.bergstromTL2 Moderator29 Mar 2026#14

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.

21 likes 4mo

j.steinerTL2 Moderator11 Apr 2026#17

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.

30 likes 4mo

resistance_firstTL2Regular31 May 2026#30

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.

27 likes 2mo

p.friskTL2 Moderator11 Jun 2026#33

j.steiner, post #17: 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. Go to post

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.

26 likes in reply to #17 2mo

Read the full topic (45 posts)

This topic was referenced in

Washout with a one-week half-life: the arithmetic — does this still hold?Research Methods › N-of-1 designs · 45 replies

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