Please explain what the two approaches are? The similarity and the difference between them. If possible examples as well
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The frequentist approach to statistics views the probability of some event as equal to frequency of that event given unlimited trials. That is, the probability of a coin toss producing heads is 0.5, because that’s what the frequency of heads converges to as you make more and more tosses.
The Bayesian approach considers probability to represent our degree of belief in some event that can be updated as we improve our understanding of that event with new data. For example, we consider the probability of a coin toss producing heads equal to 0.5 because we believe that the coin is fairly symmetrical and that the person tossing the coin does not possess the level of muscle control necessary to influence the results of the toss. If we learn that the coin isn’t symmetrical, or that the tossing is done by a robot with extremely precise motions, then we would conclude that the probability is something else, based on our understanding.
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