Topic summary

Blinding yourself: practical methods and their limits — the long version

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

This is a generated summary. It shows the 5 most-liked posts from a topic of 12, 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.

np_gilmoreTL3Nurse practitioner31 May 2026 · edited#3

s.diallo, post #2: the opening post is right about the mechanism and I think understates the practical bit. 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… 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.

12 likes in reply to #2 2mo

m.vukovicTL2 Moderator21 Jun 2026#6

post #5 answers the question as asked. The question underneath it is different.

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.

25 likes 1mo

priorauth_notesTL2Regular28 Jun 2026#7

m.vukovic, post #6: post #5 answers the question as asked. The question underneath it is different. 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. Go to post

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.

7 likes in reply to #6 30d

l.piresTL2 Moderator16 Jul 2026#10

m.dalgaard, post #5: 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. Go to post

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.

18 likes in reply to #5 12d

f.sjobergTL2 Moderator21 Jul 2026#11

post #10 answers the question as asked. The question underneath it is different.

I disagree with the reply above, and I think the disagreement is substantive rather than terminological.

The distinction being drawn does not survive when you look at the published data for this specific question. I would be glad to be shown wrong on this, because the version I am arguing against is more convenient.

20 likes 7d

Read the full topic (12 posts)

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