a more complex model than M1 because it has a free parameter which allows it to model the data more closely. The ability of Bayes factors to take this into account is a reason why Bayesian inference has been put forward as a theoretical justification for and generalisation of Occam's razor, reducing Type I errors. On the other hand, the modern method of relative likelihood takes into account the number of free parameters in the models, unlike the classical likelihood ratio. The relative likelihood method could be applied as follows. Model M1 has 0 parameters, and so its Akaike information criterion (AIC) value is . Model M2 has 1 parameter, and so its AIC value is . Hence M1 is about times as probable as M2 to minimize the information loss. Thus M2 is slightly preferred, but M1 cannot be excluded. See also Akaike information criterion Approximate Bayesian computation Bayesian information criterion Deviance information criterion Lindley's paradox Minimum message length Model selection Statistical ratios Odds ratio Relative risk References Further reading Dienes, Z. (2019). How do I know what my theory predicts? Advances in Methods and Practices in Psychological Science Jaynes, E. T. (1994), Probability Theory: the logic of science, chapter 24. External links BayesFactor —an R package for computing Bayes factors in common research designs Bayes factor calculator — Online calculator for informed Bayes factors Bayes Factor Calculators —web-based version of much of the BayesFactor package Factor Model selection Statistical ratios