An Introduction to Statistics with Python [PDF]

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An Introduction to Statistics with Python

So far we have restricted ourselves to the frequentist interpretation, which interprets p as the frequency of an occurrence: if an outcome of an experiment has the probability p, it means that if that experiment is repeated N times (where N is a large number), then we observe this specific outcome Np times.

Or in other words: given a certain model, we look at the likelihood to find the observed set of data. The Bayesian interpretation of p is quite different and interprets p as our belief of the likelihood of a certain outcome.

Here we take the observed data as fixed and look at the likelihood to find certain model parameters. For some events, this makes a lot more sense. For example, a presidential election is a one-time event, and we will never have a large number of N repetitions.