The saddlepoint approximation to the likelihood, and its corresponding maximum likelihood estimate (MLE), offer an alternative estimation method when the true likelihood is intractable or computationally expensive. However, maximizing this approximated likelihood instead of the true likelihood inevitably comes at a price: a discrepancy between the MLE derived from the saddlepoint approximation and the true MLE. In previous studies, the size of this discrepancy has been investigated via simulation, or by engaging with the true likelihood despite its computational difficulties. Here, we introduce an explicit and computable approximation formula for the discrepancy, through which the adequacy of the saddlepoint-based MLE can be directly assessed. We present examples demonstrating the accuracy of this formula in specific cases where the true likelihood can be calculated. Additionally, we present asymptotic results that capture the behaviour of the discrepancy in a suitable limiting framework.

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