We have introduced a novel method for estimation of selection strengths that explicitly incorporates uncertainty in estimated alignments. By integrating a statistical alignment procedure into our parameter estimation, we do not rely on a fixed alignment input. Instead, we calculate the expected number of observations, and are thus weighting our parameter estimates by the probability of each possible alignment. We naturally can not compete with knowing the true alignment, something which sufficient and extremely time consuming manual work can get close to. We do however offer a fast, automatic and efficient alternative to the use of a fixed alignment, which provides a quick and easy way for producing selection factors for different regions in a viral genome. We outperform alignments given by ClustalW consistently. We even beat GenAl for sequences of evolutionary distance below 0.8 and only do slightly worse for ones further apart. It is however additionally worth noting that the sequences we have and generally will be dealing with, will generally The average selection acting on each of the seven regions of the Hepatitis B genome, measured by weighing each expected mutation by its appropriate selection coefficient. Selection on double coding regions appears to tend to be more lenient have an evolutionary distance of 0.4 -0.9. We are therefore encouraged to see that our method is competetive compared to the slightly more refined GenAl and hope that this is amplified once also extended to include protein alignment. More importantly our method is statistical, which means it can be more readily incorporated into a maximum likelihood estimation framework, whereas GenAl works on a count-basis.