Bland-Altman Plot
Chart overview
The Bland-Altman plot, introduced by Martin Bland and Douglas Altman in 1986, is the preferred method for assessing the agreement between two quantitative measurement techniques rather than their correlation.
Key points
- The x-axis shows the mean of the two measurements, and the y-axis shows their difference.
- A horizontal line at the mean difference (bias) indicates systematic offset between methods.
- The limits of agreement (mean difference +/- 1.
Python Tutorial
How to create a bland-altman plot in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
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"Create a Bland-Altman plot comparing two measurement methods from my dataset. Calculate and plot the mean difference (bias) as a solid horizontal line and the 95% limits of agreement (mean +/- 1.96 SD) as dashed lines. Shade the region between the limits of agreement. Label the bias and LOA values on the right margin. Test for proportional bias with a regression line. Format for a clinical validation paper at 300 DPI."
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Python Code Example
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Common Use Cases
- 1Validating a new point-of-care blood glucose meter against laboratory reference analyzer
- 2Comparing wearable accelerometer step counts against research-grade pedometer
- 3Assessing agreement between manual and automated cell counting in hematology
- 4Evaluating a new MRI-based volume measurement against CT gold standard in radiology
Pro Tips
Never use correlation coefficients (r or R-squared) to assess agreement - use Bland-Altman
Check for proportional bias by regressing the differences on the means and testing its slope
Report the 95% limits of agreement with their own confidence intervals for full uncertainty quantification
If differences are not normally distributed, consider using a non-parametric version with percentile limits
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 bland-altman-plot.
numpy
Useful in specialized workflows that complement core Python plotting libraries for bland-altman-plot analysis tasks.
pandas
Good for quick exploratory drafts directly from DataFrame operations before polishing in matplotlib or plotly.
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.