Topic summary

Second pass at: Regression to the mean in progress reports

Research Methods › Statistics effect size worked example heterogeneity

This is a generated summary. It shows the 9 most-liked posts from a topic of 76, 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.

am.wikstromTL2 Moderator22 Dec 2024#6

post #5 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.

29 likes 19mo

r.mensahTL2 Moderator13 Jan 2025#23

I read post #21 twice before replying, because I had assumed the opposite.

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.

31 likes 18mo

n.abernathyTL3Analytical chemist20 Jan 2025#30

d.magalhes, post #5: On the opening post — agreed on the reasoning, with one qualification. 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. Go to post

32 likes in reply to #5 18mo

a.salcedoTL3Regular27 Jan 2025#37

post #36 answers the question as asked. The question underneath it is different.

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.

25 likes 18mo

trough_indexTL3Regular7 Feb 2025#48

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".

28 likes 18mo

ew.kuuselaTL2 Moderator10 Feb 2025#51

post #50 is right about the mechanism and I think understates the practical bit.

Confidence intervals: rather than a single point estimate, a range of plausible values. A narrow interval means precise measurement; a wide interval means measurement is imprecise. Wider intervals (more uncertainty) are honest about limitation.

29 likes 18mo

glossary_deskTL3Regular19 Feb 2025#60

Confounding: a third variable explains an apparent association. In randomised data, randomisation balances confounders. In observational data, confounders can be adjusted for but unknown ones cannot.

30 likes 17mo

k.radichTL2 Moderator26 Feb 2025#68

Buchholz, post #58: 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. Go to post

This follows post #65 rather than contradicting it.

Confounding: a third variable explains an apparent association. In randomised data, randomisation balances confounders. In observational data, confounders can be adjusted for but unknown ones cannot.

33 likes in reply to #58 17mo

e.krastevTL2 Moderator1 Mar 2025#72

n.silva, post #63: 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. Go to post

Coming back to post #70, because the follow-up matters more than the original answer.

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.

26 likes in reply to #63 17mo

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[2026 update] Correlation in a self-tracked dataset: what it can supportResearch Methods › Statistics · 2 replies

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