Topic summary

Correlation in a self-tracked dataset: what it can support

Research Methods › Statistics effect size heterogeneity worked example number needed to treat Closed

This is a generated summary. It shows the 9 most-liked posts from a topic of 167, 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.

orbitrap_olaTL3Mass spectrometrist20 Aug 2024#7

g.tamm, post #6: 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. 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.

31 likes in reply to #6 23mo

b.kowalskiTL2 Moderator20 Aug 2024#11

MSaarinen, post #9: 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. Go to post

This follows post #8 rather than contradicting it.

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".

29 likes in reply to #9 23mo

m.guerreroTL2 Moderator21 Aug 2024 · edited#25

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 #9 23mo

m.onwukaTL2 Moderator21 Aug 2024#39

g.pemberton_uk, post #30: P-values and significance: p Go to post

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.

32 likes in reply to #30 23mo

r.ekstromTL2 Moderator23 Aug 2024#67

NHuddleston, post #26: Picking up post #23: that is the part I would want checked first. 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. Go to post

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.

31 likes in reply to #26 23mo

a.coelhoTL2 Moderator24 Aug 2024#93

i.boateng, post #15: 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. Go to post

post #92 is right about the mechanism and I think understates the practical bit.

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.

28 likes in reply to #15 23mo

a.villalobosTL2 Moderator24 Aug 2024#99

This follows post #96 rather than contradicting it.

I disagree with the reply above, and I think the disagreement is substantive rather than terminological.

29 likes 23mo

a.kirchnerTL2 Moderator25 Aug 2024 · edited#125

e.kuipers, 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

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 #4 23mo

n.kaufmannTL2 Moderator26 Aug 2024#165

n.kuusela, post #8: 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

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.

29 likes in reply to #8 23mo

Read the full topic (167 posts)

This topic was closed 45 days after the last reply. Closing is automatic for quiet topics so that a settled answer does not collect new questions underneath it. If you have a follow-up, open a new topic and link back to this one — that keeps both readable and gives your question its own title.

Moved from N-of-1 designs by t.vasquez. 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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