One of the main applications of the Bland-Altman plot is to compare two clinical measurements, each of which has produced an error in its measurements.  It can also be used to compare a new technique or measurement method with a gold standard, because even a gold standard does not imply it without error – and should not involve it.  Software that provides Bland Altman plots is available on Analysis-it, MedCalc, NCSS, GraphPad Prism, R or StatsDirect. This is Wikipedia`s definition of a plot of Bland Altman: the tutorial deals with setting up the relationship between methods, estimating average distortion and match limits, understanding the importance of repeatability, using replication measures, managing a relationship between difference and size, changing measures to remove a relationship, estimating regression-based compliance limits, and estimating parameter compliance limits. Keywords: Bland-Altman-Plot, line of agreement, two measures of the Bland-Altman plots are generally interpreted informally, without any further analysis. Ask yourself these questions: Bland and Altman indicate that two methods developed to measure the same parameter (or property) should have a good correlation if a group of samples is selected so that the property to be determined varies considerably. Therefore, a high correlation for two methods of measuring the same property could in itself be only a sign that a widely used sample has been chosen. A high correlation does not necessarily mean that there is a good agreement between the two methods. The Bland Altman plot is better known than Tukey Mean-Difference Plot (one of many diagrams developed by John Tukey en.wikipedia.org/wiki/John_Tukey). What are the advantages of using a Bland-Altman parcel compared to other methods of comparing two different measurement methods? Learn how to evaluate the match between two measurement methods using it analytics for Microsoft Excel.
What is the average difference between methods (distortion)? You have to interpret it clinically. Is the gap large enough to be large? It`s a clinical question, not a statistic.