Topic summary

Sample size intuition for a personal experiment — does this still hold?

Research Methods › Statistics effect size heterogeneity number needed to treat Solved

This is a generated summary. It shows the 6 most-liked posts from a topic of 39, 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.

c.ostergaardTL2 Moderator Solution11 Mar 2026 · edited#7

m.strand_rph, post #2: the opening post answers the question as asked. The question underneath it is different. Power and sample size: a study might be too small to detect a real effect (low power). Sample size calculations help determine how many participants are needed to detect an effect of a given magnitude. Go to post

Two things before anyone answers the substance.

First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound.

7 likes in reply to #2 5mo

hana.satoTL4 Moderator15 Mar 2026#8

Staff post. Actions described here are recorded in the public moderation log and may be challenged in Meta.

This follows post #5 rather than contradicting it.

Number needed to treat: how many people need to be treated to prevent one bad outcome or achieve one good outcome. More intuitive than relative risk reduction.

26 likes 4mo

methods_marginTL3Regular2 May 2026#20

Two things before anyone answers the substance.

First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound.

33 likes 3mo

IMainwaringTL3Regular27 May 2026#27

Multiplicity and multiple comparisons: if you test many hypotheses, the chance of finding a false positive by random chance increases. That is why pre-specifying the primary hypothesis matters.

32 likes 2mo

f.chowdhuryTL2 Moderator6 Jun 2026#30

Absence of evidence and evidence of absence: if a study is small and finds no effect, that is absence of evidence, not evidence of absence. A larger study might find an effect that a small study missed.

25 likes 2mo

s.demirTL2 Moderator16 Jun 2026#33

post #32 answers the question as asked. The question underneath it is different.

Number needed to treat: how many people need to be treated to prevent one bad outcome or achieve one good outcome. More intuitive than relative risk reduction.

26 likes 1mo

Read the full topic (39 posts)

Moved from N-of-1 designs by dr_okonkwo. Category placement is not obvious from outside and getting it wrong is expected. This topic will get better answers here. The move is recorded in the public log citing R7.

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