We test the confidence levels of our predictions, trying to create as 'realistic' simulated data as possible. In the light of our real data analysis, we take the Hepatitis B genome NC00397 and split it into 7 different regions, a new one starting whenever there is a change in gene structure. We evolve the sequence according to our indel model with varying transition and transversion rate of a = 0.2 -0.8 and b = a/2 respectively, and fixed selection strength of 0.5 for each of the different regions. Depending on the evolutionary distance and closely related to our results in Figure  1 , we achieve an accuracy of approximately 70 -94% with both the statistical alignment method as well as the fixed alignment method using GenAl, versus 20 -72% for the fixed alignment method using ClustalW. In contrast using the true alignment gives us an accuracy of 78 -96%. Here our estimate is counted as correct if the true value lies within the error bars around the estimated value. This is naturally highly dependent on the width of our error bars, which in some cases are indeed large, simply due to lack of data. However, the error bars for the parameter estimates of both the fixed and the summed alignment are close to identical, and thus the measure is valid if only for the sake of direct comparison.