Topic summary

Measurement error in home scales, with a worked standard deviation

Research Methods › Statistics effect size heterogeneity number needed to treat confounding worked example Solved Wiki

This is a generated summary. It shows the 5 most-liked posts from a topic of 32, 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.

t.vargaTL2 Moderator5 Feb 2026#3

SHermansen, post #2: the opening post answers the question as asked. The question underneath it is different. 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. 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.

31 likes in reply to #2 6mo

an.zamoraTL2 Moderator Solution14 Feb 2026#7

methods_draft, post #4: 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. Go to post

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.

7 likes in reply to #4 5mo

e.kimaniTL2 Moderator5 Mar 2026#18

Coming back to post #16, because the follow-up matters more than the original answer.

25 likes 5mo

v.krastevTL2 Moderator14 Mar 2026#24

Knowlton, post #17: Picking up post #14: that is the part I would want checked first. 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

Picking up post #21: that is the part I would want checked first.

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.

30 likes in reply to #17 4mo

NardoneTL2Member26 Mar 2026#32

k.haddad, post #27: I read post #25 twice before replying, because I had assumed the opposite. Regression to the mean: if you select people with extreme values (very high or very low), their next measurement is often less extreme just by chance. This can look like a treatment effect when it is just statistics. 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.

32 likes in reply to #27 4mo

Read the full topic (32 posts)

This topic was referenced in

About the Statistics categoryResearch Methods › Statistics · 8 replies
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