Interesting tour of Akaike’s information criterion (AIC) and its relationship with (log-)likelihood ratio tests, by Chris Sutherland et al. (in press).
My fav bit is where the authors show that an improved AIC for a model comparison where the bigger model has just one extra parameter is equivalent to p < .157 on the LR-test – because simple arithmetic.
Sutherland, C., Hare, D., Johnson, P. J., Linden, D. W., Montgomery, R. A., & Droge, E. (in press). Practical advice on variable selection and reporting using AIC. Proceedings of the Royal Society B: Biological Sciences.