The diagram shows a dispersal diagram of the differences represented by the average values of the two measures. The horizontal lines are drawn at the average difference and the limits of the match. Bland-Altman plots are widely used to assess the agreement between two instruments or two measurement techniques. Bland-Altman plots identify systematic differences between measures (i.e. fixed pre-stress) or potential outliers. The average difference is the estimated distortion, and the SD of the differences measures random fluctuations around this average. If the average value of the difference based on a 1-sample-t test deviates significantly from 0, this means the presence of a solid distortion. If there is a consistent distortion, it can be adjusted by subtracting the average difference from the new method. It is customary to calculate compliance limits of 95% for each comparison (average difference ± 1.96 standard deviation of the difference), which tells us how much the measurements were more likely in two methods for most people. If the differences in the average± 1.96 SD are not clinically important, the two methods can be interchangeable. The 95% agreement limits can be unreliable estimates of population parameters, especially for small sampling sizes, so it is important to calculate confidence intervals for 95% compliance limits when comparing methods or evaluating repeatability. This can be done by the approximate Bland and Altman method  or by more precise methods.
 We will see in this section how to adjust the original Bland-Altman plot to apply it to an A/B test. I will first explain how the diagram is created in its original version , , and then I will explain how to create it with the data from our A/B tests. In their paper, they also showed why the correlation coefficient, the statistical test of means comparison and regression are not appropriate to decide on the agreement of two measures that would be in our case of A/B test to decide the power of the challenger over that of the champion. A Bland-Altman plot (differential diagram) in analytical chemistry or biomedicine is a method of data representation used in the analysis of the agreement between two different trials. It is identical to a tube of average difference Tukey, the name under which it is known in other areas, but it was popularized in the medical statistics of J. Martin Bland and Douglas G. Altman.   Bland-Altman plots have also been used to investigate the possible correlation between measurement deviations and actual value (i.e.
proportional distortion). The existence of proportional distortion indicates that the methods do not uniformly correspond to the range of measures (i.e., the limits of compliance depend on the actual measure). To formally assess this relationship, the difference between methods should be reduced to the average of the two methods.