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Topic summary

Second pass at: Absence of evidence and evidence of absence

This is a generated summary. It shows the 8 most-liked posts from a topic of 56, 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.
GV
g.valckenaereTL3Regular20 May 2026#6

Coming back to post #4, because the follow-up matters more than the original answer.

Effect sizes: the magnitude of a difference, not just whether it is statistically significant. A difference that is significant (p<0.05) might be too small to matter. A large effect might not be significant if sample size is small.

27 likes 2mo
BV
bias_varianceTL4Biostatistician7 Jun 2026#17
s.leclerc, post #1: On the subject in the title: Second pass at: Absence of evidence and evidence of absence Working notes rather than a conclusion. Session topic: SELECT ( N Engl J Med , 2023). Please read it before posting; the discussion is much better when everyone has. The question I would like us to start with is what the trial set out to estimate,… Go to post

Relative risk and odds ratios: both compare the rate in one group to the rate in another. Relative risk is easier to understand. Odds ratios are standard in many analyses but can be misinterpreted.

29 likes in reply to #1 2mo
RH
revision_historyTL3Wiki editor16 Jun 2026#23
s.teixeira, post #7: 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

Picking up post #20: that is the part I would want checked first.

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.

27 likes in reply to #7 1mo
N
NardoneTL2Member29 Jun 2026#33

Worth separating two things that post #29 runs together.

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.

25 likes 28d
RM
r.molnarTL2 Moderator8 Jul 2026#40
Tamburello, post #37: Effect sizes: the magnitude of a difference, not just whether it is statistically significant. A difference that is significant (p Go to post

This follows post #37 rather than contradicting it.

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.

26 likes in reply to #37 20d
DB
da.bakkerTL2 Moderator12 Jul 2026#43
g.valckenaere, post #6: Coming back to post #4, because the follow-up matters more than the original answer. Effect sizes: the magnitude of a difference, not just whether it is statistically significant. A difference that is significant (p Go to post

post #42 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.

32 likes in reply to #6 16d
MO
m.oyelaranTL2 Moderator17 Jul 2026#47

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.

24 likes 11d
K
KForsbergTL2Member25 Jul 2026#54

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.

22 likes 3d

Read the full topic (56 posts)

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