The main advantage of the frequentist approach is that you can do the calculations much more easily. Bayesian statistics is great, but often the calculations ar

The main advantage of the frequentist approach is that you can do the calculations much more easily. Bayesian statistics is great, but often the calculations are much more difficult because of your distribution of priors. You can make up simplified priors to ease the calculations, but then you run into some of the same problems as frequentist statistics. Here is a simple example: lets say you flip a coin 10 times and get 8 heads, what is the probability that the coin is not fair? In frequentist statistics you only have to calculate the likelihood of the results for a null hypothesis, and then you use a p-value. While this approach is flawed, at least you can quickly do the calculation and get an approximate answer. In Bayesian statistics you have to specify the prior distribution, and calculate the likelihood of your results under every possible hypothesis. Correctly specifying this prior distribution and calculating the results is quite challenging - especially if you want to use a realistic prior (not just uniform). This is a pretty simple example, you can imagine how much more challenging this becomes in real-world problems. On the other hand, it is true that the frequentist approach doesn't really answer the question asked, so it is misleading (especially if you choose a p-value that isn't specific to the problem). If you choose p-values based on prior knowledge, than the differences between frequentist and bayesian are less extreme.