Frequentist versus Bayesian view of the world: A simple explanation

The frequentist approach forms an expectation from a sample of data. The Bayesian approach uses some knowledge to form a prior.  In the case of the frequentist, a sample of data is taken or observed and from that sample conclusions are drawn. In the Bayesian approach, there is a start with some knowledge, and data are used to update the prior knowledge. Conflicting evidence will lead to an update of priors. 

The frequentist assumes events are based on frequencies, the count, while Bayesian inference will draw on prior knowledge. The frequentist does not calculate the probability of a hypothesis. He accepts or rejects and is an absolutist. The Bayesian always thinks in terms of probabilities and reasons in relative differences.

The frequentist believes a parameter is not a random variable. The Bayesian says that a parameter is a random variable and measure likelihoods.

The frequentist will talk about a confidence interval, p-value, power, and significance. The Bayesian will use the term creditable interval, prior, and posterior. The frequentist will think about action to take, accept or reject hypotheses, and getting a right answer. The Bayesian will discuss opinions or prior beliefs and how they may be updated. There are no right answers only more or less likely answers.

If you are a trader, you are more likely to be a Bayesian and think about probabilities and priors.

from KDnuggests