It's because practicioners of one says that the other camp is wrong and question each other's methodologies. And in academia, questioning one's methodology is akin to saying one is dumb.
To understand both camps I summarize like this.
Frequentist statistics has very sound theory but is misapplied by using many heuristics, rule of thumbs and prepared tables. It's very easy to use any method and hack the p-value away to get statistically significant results.
Bayesian statistics has an interesting premise and inference methods, but until recently with the advancements of computing power, it was near impossible to do simulations to validate the complex distributions used, the goodness of fit and so on. And even in the current year, some bayesian statisticians don't question the priors and iterate on their research.
I recommend using methods both whenever it's convenient and fits the problem at hand.
To understand both camps I summarize like this.
Frequentist statistics has very sound theory but is misapplied by using many heuristics, rule of thumbs and prepared tables. It's very easy to use any method and hack the p-value away to get statistically significant results.
Bayesian statistics has an interesting premise and inference methods, but until recently with the advancements of computing power, it was near impossible to do simulations to validate the complex distributions used, the goodness of fit and so on. And even in the current year, some bayesian statisticians don't question the priors and iterate on their research.
I recommend using methods both whenever it's convenient and fits the problem at hand.