Research Methods · Statistics

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Research Methods › Statistics effect size number needed to treat heterogeneity

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s.leclercTL4 Moderator23 Sep 2025#1

Community wiki post. Any member at trust level 3 or above can edit this post; every edit is recorded. Last edited by hana.sato on 26 Feb 2026.

27 Sep 2025 — s.leclerc: Restructured into sections so the outline is navigable.
8 Nov 2025 — mira.patel: Added the limitations paragraph that review asked for.
26 Feb 2026 — hana.sato: Corrected an arithmetic slip in the second example.

Editors: s.leclerc, mira.patel, hana.sato

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

Effect sizes, intervals, multiplicity, and the difference between absent and undetected.

This post is a community wiki: any member at trust level 3 or above can edit it, and every edit is recorded with its author and a note. If the scope described here has drifted from what actually gets posted, edit it rather than starting a topic about it.

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0 likes 10mo

j.mwangiTL4 Moderator10 Oct 2025#2

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

Moderation notes for this category, so that nothing about how it is run is a surprise.

Topics get moved into and out of here regularly. A move is not a judgement — the category tree is not obvious from outside and putting a question in the wrong place is expected. When I move something I say so in the topic and the action appears in the public log with the guideline cited.

The two guidelines that come up most often in this category are R2, on supplying a source when asked, and R7, on category placement. Neither is used punitively.

1 like 10mo

b.vestergaardTL2 Moderator22 Oct 2025#3

As a member rather than staff: the most useful thing I did when I started reading this category was to work through the tag pages rather than the topic list. The topic list shows you what is recent. The tags show you what is settled.

10 likes 9mo

CSagredoTL3Regular2 Nov 2025#4

Suggestion for the wiki post above: it would be worth linking the two or three most-referenced topics in this category directly, since new members reliably ask for exactly those. Happy to make the edit myself if nobody objects, with a revision note.

23 likes 9mo

a.vestergaardTL2 Moderator12 Nov 2025#5

s.leclerc, post #1: Effect sizes, intervals, multiplicity, and the difference between absent and undetected. This post is a community wiki: any member at trust level 3 or above can edit it, and every edit is recorded with its author and a note. If the scope described here has drifted from what actually gets posted, edit it rather than starting a topic… Go to post

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.

32 likes in reply to #1 8mo

glossary_deskTL3Regular21 Nov 2025#6

Having read the exchange above, I think I was wrong earlier in this topic and I want to say so plainly rather than quietly editing.

The correction was fair and I had been repeating something I had not checked carefully enough.

0 likes 8mo

l.krastevTL2 Moderator30 Nov 2025#7

P-values and significance: p<0.05 means the data would be surprising if the null hypothesis were true, not that the null hypothesis is false. A non-significant p-value does not mean "no effect".

6 likes 8mo

glossary_checkTL2Member9 Dec 2025#8

l.krastev, post #7: P-values and significance: p Go to post

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.

16 likes in reply to #7 8mo

y.rahimiTL2 Moderator17 Dec 2025#9

post #8 is right about the mechanism and I think understates the practical bit.

For anyone arriving from a search: the marked solution above is the direct answer, and the replies underneath it add the caveats that make it safe to use.

24 likes in reply to #1 7mo

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