This study examines the use of age-structured maximum likelihood and Bayesian approaches for stock assessment of the Namibian monkfish, Lophius vomerinus, resource with questionable data, in which time series are short, abundance indices are variable, and research data conflict with commercial data. Bayesian approaches with both noninformative and informative priors are investigated to determine ... [Show full abstract] if they enhance estimation stability. Three data scenarios are assessed: commercial and research survey data, research survey data only, and commercial data only. Both statistical approaches show that resource abundance has decreased with exploitable biomass estimated at approximately 44% of pristine levels. The maximum likelihood and the Bayesian approach with noninformative priors result in similar estimates. As the abundance data contained little information pertaining to possible density dependence within the stock–recruit relationship, only a Bayesian approach with informative priors reduces uncertainty in the steepness parameter h. Estimated management quantities are sensitive both to the set of data sources and whether prior information was informative or not. The strengths of the Bayesian approach include the integration of prior information with uncertain data, the exploration of data conflicts, and the ability to show the uncertainty in estimates of management parameters. Its weakness is that estimation stability is dependent on the choice of priors, which alters some posterior distributions of management quantities.