Likelihood Principle

Definition: The notion that all information relevant to inference contained in data is provided by the likelihood. The principle suggests that the likelihood function can be used to compare the plausibility of various parameter values. While Bayesians and likelihood theorists subscribe to the likelihood principle, Neyman-Pearson theorists do not, as significance tests violate the likelihood principle because they take into account information not in the likelihood.

Related terms: <a href='/glossary/bayesian-inference/'>Bayesian inference</a>, <a href='/glossary/likelihood-function/'>Likelihood Function</a>

References: Dienes (2008), Geyer (2003, 2007), &

Drafted and Reviewed by: Alaa Aldoh, Sam Parsons, FlĂĄvio Azevedo

Note that we are currently working on an automated mechanism to link references cited above with their full-length version that can be found at with all references used so far.