Forest Plot
Updated 2026-08-13
INTRODUCTION
English translation pending.
CORE DEFINITION
A forest plot displays the results of multiple studies on a common scale, with each study shown as a point estimate and a horizontal line representing its confidence interval, and a diamond at the bottom representing the pooled estimate. The core proposition is that a reader should see the spread between studies rather than only the pooled result, because heterogeneity determines whether pooling is meaningful at all. The key qualification is that the diamond summarizes but does not resolve disagreement, and interval width depends on study precision.
SCAFFOLDING EFFECT
Reduce cognitive load
- Interval read: check whether each study's line crosses the no-effect line rather than reading only the point. - Spread check: look at how widely the individual estimates disagree before trusting the pooled value. - Heterogeneity report: quote the heterogeneity statistic alongside the pooled estimate whenever you cite it.
Anchor fast decisions
Plotting every study on one axis makes the dispersion of estimates visible, so a pooled number that hides substantial disagreement is immediately recognizable. Confidence interval length communicates precision, so a wide line signals a small or noisy study. The diamond then shows where the weighted centre lies and whether it clears the no-effect line.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Forest_plotverified
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