A Bland–Altman plot (difference plot) in analytical chemistry or biomedicine is a method of data plotting used in analyzing the agreement between two different assays. It is identical to a Tukey mean-difference plot, the name by which it is known in other fields, but was popularised in medical statistics by J. Martin Bland and Douglas G. Altman.

Consider a sample consisting of ${\displaystyle n}$ observations (for example, objects of unknown volume). Both assays (for example, different methods of volume measurement) are performed on each sample, resulting in ${\displaystyle 2n}$ data points. Each of the ${\displaystyle n}$ samples is then represented on the graph by assigning the mean of the two measurements as the ${\displaystyle x}$-value, and the difference between the two values as the ${\displaystyle y}$-value.

The Cartesian coordinates of a given sample ${\displaystyle S}$ with values of ${\displaystyle S_{1}}$ and ${\displaystyle S_{2}}$ 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. Log transformation (base 2) of the measurements before the analysis will enable the standard approach to be used; so the plot will be given by the following equation:

This version of the plot is used in MA plot.

Interpretation of a Bland-Altman plot is contingent on the construction of the plot and data at hand. Variations to the default plot have introduced throughout the years and each should be interpreted accordingly.

The original plot displays a scatter plot of differences between individual data points. The differences should be of the new reference system minus a gold standard. An average of the differences is plotted horizontally with limits of agreement plotted parallel to this mean difference line. The limits of agreement represent a confidence interval for which most of the differences lie between systems. The mean difference represents a general bias between the two systems; a positive mean difference indicates the reference system generally produces larger values relative to the golden standard, and a negative mean difference indicating the reference system generally produces lower values than the verified system. A mean difference closet to 0 indicates agreement between two systems, though the limits of agreement illustrate more nuance.

Since the limits of agreement are by-default contingent on the standard deviation of the data, the distribution of the differences must follow a normal distribution. In the event that the distribution of differences are not normal, limits of agreement not contingent on normal distribution may be used instead. Bland and Altman's follow up paper on the topic explains that percentile of differences are a suitable replacement in such cases.