I guess it depends on the context in which it is applied. It is anecdotal, but in the industries where I worked, people trying to introduce Bayesian statistics

I guess it depends on the context in which it is applied. It is anecdotal, but in the industries where I worked, people trying to introduce Bayesian statistics did not have a higher chance of their results being interpreted properly. If you do A/B testing and don't do pre-registration or power analysis, what are the chances that you can/will be able to explain the nuances of probabilistic reasoning ? I agree w/ the parent poster than the fundamental issue is probability: if you are talking w/ people w/o background in stats, you will have a really hard time to go beyond a true/false statement. Moreover, one of the most effective (in $ terms) application of statistics in recent times is A/B testing. While you can do "Bayesian A/B testing", the basic methodology is fundamentally frequentist. Mistakes there can be hedged through better tooling / UX (to avoid peeking, etc.), as effectively as using Bayesian statistics.