As for all statistical methods, a number of assumptions and approximations are inherently required for the application of ABC-based methods to real modeling problems. For example, setting the tolerance parameter formula_80 to zero ensures an exact result, but typically makes computations prohibitively expensive. Thus, values of formula_66 larger than zero are used in practice, which introduces a bias. Likewise, sufficient statistics are typically not available and instead, other summary statistics are used, which introduces an additional bias due to the loss of information. Additional sources of bias- for example, in the context of model selection—may be more subtle.