Bland–Altman plot

It is identical to a Tukey mean-difference plot,[1] the name by which it is known in other fields, but was popularised in medical statistics by J. Martin Bland and Douglas G.

Both assays (for example, different methods of volume measurement) are performed on each sample, resulting in

determined by the two assays is For comparing the dissimilarities between the two sets of samples independently from their mean values, it is more appropriate to look at the ratio of the pairs of measurements.

[4] See Analyse-it, MedCalc, NCSS, GraphPad Prism, R, StatsDirect, or JASP for software providing Bland–Altman plots.

Bland–Altman plots are extensively used to evaluate the agreement among two different instruments or two measurements techniques.

Bland–Altman plots allow identification of any systematic difference between the measurements (i.e., fixed bias) or possible outliers.

If there is a consistent bias, it can be adjusted for by subtracting the mean difference from the new method.

The 95% limits of agreement can be unreliable estimates of the population parameters especially for small sample sizes so, when comparing methods or assessing repeatability, it is important to calculate confidence intervals for 95% limits of agreement.

[6] Bland–Altman plots were also used to investigate any possible relationship of the discrepancies between the measurements and the true value (i.e., proportional bias).

When a relationship between the differences and the true value was identified (i.e., a significant slope of the regression line), regression-based 95% limits of agreement should be provided.