This page implements an online version of John Kruschke's Bayesian estimation supersedes the t-test (BEST), a Bayesian model that can be used where you classically would use a two-sample t-test. BEST estimates the difference in means between two groups and yields a probability distribution over the difference. From this distribution we can take the mean credible value as our best guess of the actual difference and the 95% Highest Density Interval (HDI) as the range were the actual difference is with 95% credibility. It can also be useful to look at how credible it is that the difference between the two groups is < 0 or > 0.
To try it out just enter some data below or run with the data that is already entered, the heights in m of the winning team of the 2012 NBA finals (group 1) and the winning team of Stanley cup 2012 (group 2). Data can be entered in almost any way, separated by spaces, commas, newlines, etc.
Even though the difference between the means of the groups usually is the main interest, BEST also estimates other parameters. Except for the means and SDs of the groups BEST estimates a measure of to what degree there are outliers in the data that makes the distribution of the data deviate from normality. This measure is labeled "Normality" below where a normality estimate < 1.5 indicates that the data isn't normally distributed. BEST is however robust to outliers to some degree while outliers are a problem for a classical t-test. More about the assumptions of BEST and the advantages of Bayesian estimation is found in Kruschke (2012).