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The controversy is decades old and largely resolved. Both methods work well on large enough data sets, Bayesian methods are far superior on small datasets

• adastra22 3y ago
  This statement doesn’t really make sense to me. A frequentist approach assumes a random (or noisy) underlying process and characterizes its probability. A Bayesian approach assumes a fixed but unknown process and updates the probability a given model matches that reality. It is ultimately the same math, if you are comparing apples to apples. Just different ways of looking at a problem. Sometimes one is better suited than the other. It’s like as if physicists were arguing over whether Cartesian or Polar coordinates were better. It’s the same damn physics, just expressed differently. In some problems one approach is easier to work with than the other, and can even make seemingly intractable problems solvable. But that doesn’t mean the other approach was “wrong.”
  • jvans 3y ago
    In many cases yes. A simple example where this is not true is if your populations have different variances. T tests are no longer valid but heterogenous variances can be modeled with a Bayesian approach