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We have developed a frequentist approach for model selection which determinesthe consistency between any cosmological model and the data using thedistribution of likelihoods from the iterative smoothing method. Using thisapproach, we have shown how confidently we can conclude whether the datasupport any given model without comparison to a different one. In this currentwork, we compare our approach with the conventional Bayesian approach based onthe estimation of the Bayesian evidence using nested sampling. We use simulatedfuture Roman (formerly WFIRST)-like type Ia supernovae data in our analysis. Wediscuss the limits of the Bayesian approach for model selection and show howour proposed frequentist approach can perform better in the falsification ofindividual models. Namely, if the true model is among the candidates beingtested in the Bayesian approach, that approach can select the correct model. Ifall of the options are false, then the Bayesian approach will select merely theleast incorrect one. Our approach is designed for such a case and we canconclude that all of the models are false.

Bayesian vs Frequentist: Comparing Bayesian model selection with a frequentist approach using the iterative smoothing method