How is Bayes Stats supposedly more "intuitive" when it requires us to think probabilistically which IS NOT intuitive for most folks? People don’t naturally think in probabilistic ways, they often form priors through point estimates because it’s less cognitively taxing to reduce everything down to single numbers. So if the fundamental premise of Bayesian Inference is a method formulated mathematically of updating our prior hypothesis from evidence presented to us as the practitioner, then why are we forced to think in a way that divorces from everyday convention? And yes, I get that "intuitive" is relative depending on who you ask but that's often one of the arguments I hear from Bayesians as to why it's superior to Frequentists.

Because Bayesianism, unlike frequentism, has a concept of how likely a hypothesis is to be true, and that's what people intuitively (and rightly) care about.
If you try to use Bayesian methods to investigate the fairness of a coin, the output is a number which claims to represent the probability that the coin is fair. This is the thing you care about.
If you try to use frequentist methods to investigate the same thing, the output is a p-value, which is the probability that you would get results at least as "extreme" as the ones you actually got conditional on the coin actually being fair. This is not the thing you care about. (In fact, it's somewhat common for people to incorrectly believe that a p-value represents the probability that the null hypothesis is true. I imagine this is because it's intuitive that you would want to know the probability that the null hypothesis is true, so people just assume that's what p-values are.)
