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The implementation of forest-based projects to mitigate greenhouse gas emissions requires the estimation of emission factors (here the difference in biomass stocks between two forest types). The estimation of these quantities using forest inventory data and allometric models implies different sources of errors that need to be prioritized to improve the precision of estimation. Using data from permanent sample plots in a tropical moist forest in central Africa and considering four allometric models with equal likelihood, the largest source of error in the estimate of the difference of biomass between intact and logged-over forest was that due to the model choice (40 % of the sum of squares). The error due to the model choice did not cancel out in the difference due to an interaction between the model’s prediction and the diameter structure of the forest. The variability in biomass between plots was the second largest source of error, but was underestimated because of post-stratification. The error due to the model choice could be reduced by weighting the models’ predictions.