Funnel Plot
Chart overview
Funnel plots display individual study effect estimates against a measure of precision (standard error or sample size), forming an inverted funnel shape when no publication bias is present.
Key points
- Systematic reviewers use asymmetry in the funnel to detect potential publication bias, small-study effects, or heterogeneity, often supplemented by Egger regression test.
- The plot is a required element in Cochrane systematic reviews and PRISMA-compliant meta-analyses.
Python Tutorial
How to create a funnel plot in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
Python Scatter Plot TutorialExample Visualization
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"Create a funnel plot from my data. Plot effect size on the x-axis and standard error (inverted, small SE at top) on the y-axis. Draw pseudo 95% confidence funnel lines around the pooled estimate and add a vertical dashed line for the overall effect. Plot each study as a circle, scaled by inverse variance weight if available. Use journal formatting with Arial font and no top or right spines."
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Python Code Example
Console Output
Figure saved: plotivy-funnel-plot.png
Common Use Cases
- 1Assessing publication bias in systematic reviews and meta-analyses of clinical trials
- 2Detecting small-study effects and heterogeneity in pharmacological intervention studies
- 3Evaluating reporting bias in diagnostic test accuracy meta-analyses
- 4Presenting Egger regression test results visually alongside statistical test output
Pro Tips
Invert the y-axis so studies with the smallest standard error appear at the top of the funnel
Draw 95% and 99% pseudo-confidence contour lines to distinguish random sampling variation from bias zones
Scale scatter point size by study weight to communicate relative contribution visually
Add a trim-and-fill adjusted estimate as a different marker shape to indicate corrected pooled effect
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for funnel-plot.
numpy
Useful in specialized workflows that complement core Python plotting libraries for funnel-plot analysis tasks.
scipy
Useful in specialized workflows that complement core Python plotting libraries for funnel-plot analysis tasks.
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.