Classical test of inference for two-group comparisons, like the t-test, often face the problem of small effect size or borderline significance. Moreover, the issue is not so clear when we cannot reject the null.
Here I would like to discuss the following paper from Wetzels et al.: How to quantify support for and against the null hypothesis: a flexible WinBUGS implementation of a default Bayesian t test, Wetzels R, Raaijmakers JG, Jakab E, Wagenmakers EJ.
I am just reading Data Analysis Using Regression and Multilevel/Hierarchical Models from Andrew Gelman and Jennifer Hill (Cambridge University Press, 6th printing 2008). The companion website includes all data sets and R code. Another useful reference for R users is Bayesian Computation with R in the Springer’s UseR series, from J. ALbert.
First of all, I found that graphical displays are overall very nicely drawn on the B&W version of the book.