The Bayesian approach to statistics is fundamentally different from the classical approach that we have been discussing. However, some aspects of the Bayesian approach can be quite helpful to other statistical approaches. In the classical approach the parameter θ is thought to be an unknown, but fixed quantity. A random sample is drawn from the population indexed by θ, and based on the observed values in the sample, knowledge about the value of θ is obtained. In the Bayesian approach θ is considered a quantity whose variation can be described by a probability distribution called the prior distribution. This is a subjective distribution, based on the experimenter's belief, and is formulated before the data are seen (hence the name "prior" distribution). A sample is then taken from a population indexed by θ and the prior distribution is updated with this sample information. The updated prior is called the posterior distribution. The updating is done using Bayes' Rule, hence the name "Bayesian statistics." Information about θ is obtained using the posterior distribution.