Priors

In Bayesian statistics, prior distributions (or priors) represent initial beliefs abouts a parameter before new evidence or data is considered. It is a probability distribution that quantifies what is already known or assumed about a parameter’s possible values. This prior knowledge can come from previous studies, expert opinion, or a general lack of information. Priors are a fundamental component of Bayesian analysis, as they are combined with new data to produce an updated understanding of the parameter. This is slightly more nuanced that a priori statistical tests, which are tests planned prior to the collection of data.

Also, see posteriors.