In general, simulation doesn't agree with data perfectly. The more the variables (and their correlations) aren't reproduced faithfully, the more the efficiency

In general, simulation doesn't agree with data perfectly. The more the variables (and their correlations) aren't reproduced faithfully, the more the efficiency determined from simulation is likely to depart from the truth. It's even possible to accidentally train an algortihm to discriminate between simulation and data rather than signal and background. It's common practice to check data/simulation agreements between variables before using them for training. Reweighting procedures are used to improve agreement.