Correcting Datasets with artificially low starting values I am working on a project where we would like to take the ratio of two measurements A/B and subject these ratios to a ranking algorithm. The ratio is normalized prior to ranking (though the ranking/normalization are not that import to my question).

In most cases measurement A (the starting measurement) is a count with values greater than 1000.  We expect an increase for measurement B for positive effects and a decrease in measurement B for negative effects.  

Here is the issue, some of our starting counts are nearly zero which we believe is an artifact of experimental preparation.  This of course leads to some really high ratios/scaling issues for these data points.  

What is the best way to adjust these values in order to better understand the real role in our experiment?  

One suggestion we received was to add 1000 to all counts (from measurement A and B) to scale the values and remove the bias of such a low starting count,  is this a viable option?  Thank you in advance for your assistance, let me know if I am not being clear enough.
Yes, the general idea is to add a baseline small count to every category. The technical term for this is Laplace smoothing. Really it's not so much of a hack, as encoding the idea that you think there is some (uniform?) prior distribution of the events occurring.