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: Bayesian inference, Likelihood Function
References:
- Dienes, Z. (2008). Understanding psychology as a science: An introduction to scientific and statistical inference. Macmillan International Higher Education.
- Geyer, C. J. (2003). Maximum Likelihood in R (pp. 1â9). Open Science Framework.
- Geyer, C. J. (2007). Stat 5102 Notes: Maximum Likelihood (pp. 1â8). Open Science Framework.
Originally drafted by: Alaa Aldoh
Reviewed by: Sam Parsons, FlĂĄvio Azevedo