It is argued that quantitative results from statistical surveys and experiments should be communicated as inferences of the model maximising the log Bayes factor against a reference model penalised by a subjectively chosen constant times the difference in model complexity. Model complexity is measured by the degrees of freedom. In this study, an efficient algorithm is proposed to select a model from among a large set of models with unit penalties in some interval. The algorithm utilizes the penalised log Bayes factor with only the likelihood ratio statistic, model dimensions and a constant. This approach seems to be a more realistic screening device than related criteria similar to the Bayesian information criterion.