This answer is probably a bit late for you, but I think this falls under the category of repeatability. If anyone disagrees, feel free to comment!
Repeatability is the extent to which the identity of the observer can be used to predict the result, or in simple terms, the consistency with which one observer differs from another.
Repeatability can also be used to quantify how closely repeated measurements of an individual resemble each other, relative to measurements from another individual. (e.g. when measuring the weight of a bird at several occasions over a year, high repeatability means that the individuals consistently differ across the course of a year).
Repeatability is quantified by the intra-class correlation coefficient (ICC). In your case, the class is the observer. R can be calculated by ANOVA, or the best-practice method, linear mixed effects modelling (LMM).
If you have a low repeatability within observers (small R, nonsignificant p-value), you can say that the observers are not biasing the result.
If you have high repeatability, you have a problem, although it is not so bad if the observer error is balanced across treatment groups (but still should be avoided if possible). This would perhaps be a good time to use a model in which the observer is specified as a random factor, so that adjustments can be made for the inter-observer differences.
Here is a very good review on the topic by Nakagawa and Schielzeth (2010): http://onlinelibrary.wiley.com/doi/10.1111/j.1469-185X.2010.00141.x/abstract
And the related R package, with support for general and generalized linear mixed modelling: