Statistical significance and explaining models to a frequentist

Doe anyone know of a good guide on presenting your findings to a frequentist who is not immediately open to Bayesian thinking?

I have thought of the following questions and responses so far?

How to we know if the particular point values for your regression are statistically significant? Well the priors I selected for my model covariates has zero as a possible point value for beta and the posterior probability curves peak at a non zero value, have a narrow range of probable values, and the point probability of beta being zero is quite low so I can be confident to 95%-probability of beta being zero if this result.

How can you be confident that your model describes the data effectivly? Given that using this model, I can draw values reasonably close to the data provided and simulated draws from a simulated population sample provide resultes that do not seem to be extrem compared to the data, I think this model is on sound footing

I am still researching effective methods for comparing models to one another as things such as Bayes factors seem to have a lost of caveats once you peak under the hood but in your collective experience, does what I am starting with make sense and is there anything else you might suggest I do?