Topic summary

Prediction intervals and why they are more honest than confidence intervals

Evidence › Meta-analyses heterogeneity risk of bias effect size publication bias meta-analysis Solved Closed

This is a generated summary. It shows the 5 most-liked posts from a topic of 26, 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.

s.radichTL231 Mar 2026#3

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

Counterpoint on prediction intervals, offered without confidence: the same observation is consistent with a much duller explanation, and nobody has ruled the dull one out.

27 likes 4mo

KForsbergTL2Member Solution3 Apr 2026#8

Worth separating two things that post #6 runs together.

Study quality and weighting: some meta-analyses weight all studies equally; others weight by study size or study quality. The choice affects the result and should be stated and justified.

7 likes 4mo

s.kimaniTL25 Apr 2026#12

Understood. Thank you for being specific about the limits of it.

22 likes 4mo

ppm_errorTL3Analytical chemist6 Apr 2026#15

Careful with the language on prediction intervals. "Not detected" and "not present" are different findings and the first is a statement about the method.

31 likes 4mo

j.falkTL210 Apr 2026#24

s.hartmann, post #7: This follows post #4 rather than contradicting it. The honest answer on prediction intervals is that it depends, and the useful part is the list of what it depends on. Four items, in rough order of how much they matter. Most people get the first two right and then argue about the fourth. Go to post

Prediction intervals is one of those subjects where the general answer and the answer for a specific case diverge, and the thread will go in circles until someone says which one is being asked for.

27 likes in reply to #7 4mo

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Individual participant data versus aggregate dataEvidence › Meta-analyses · 34 replies

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