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The idea is interesting. More work is required. Practical implications would be: - Opens up the possibility to use MCMC algorithms sampling parameters given data, e.g., Stan or WinBUGS, for frequentist hypothesis testing. Conceptual implications would be: - Formally relate results from Bayesian statistics to those from frequentist statistics. - Define approaches and situations, in which frequentist and Bayesian approaches give identical results, or to explain differences obtained with different approaches. References: - Kass RE, Wasserman L (1996) The Selection of Prior Distributions by Formal Rules. JASA, 91 (435), 1343-1370 - Berger, James. \"The case for objective Bayesian analysis.\" Bayesian Analysis 1.3 (2006): 385-402. - Bernardo, José M. \"Intrinsic credible regions: An objective Bayesian approach to interval estimation.\" Test 14.2 (2005): 317-384. - Efron, Bradley. \"A 250-year argument: Belief, behavior, and the bootstrap.\" Bulletin of the American Mathematical Society 50.1 (2013): 129-146. - Berger, James O, Brunero Liseo, and Robert L Wolpert. \"Integrated likelihood methods for eliminating nuisance parameters.\" Statistical Science 14.1 (1999): 1-28. - Severini, Thomas A. \"Integrated likelihood functions for non-Bayesian inference.\" Biometrika 94.3 (2007): 529-542. - Berger, James O, José M Bernardo, and Dongchu Sun. \"The formal definition of reference priors.\" The Annals of Statistics (2009): 905-938. - Bjørnstad, Jan F. \"On the generalization of the likelihood function and the likelihood principle.\" Journal of the American Statistical Association 91.434 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