The visualization shows a Bayesian two-sample *t* test, for simplicity the variance is assumed to be
known. It illustrates both Bayesian estimation via the posterior distribution for the effect, and Bayesian
hypothesis testing via Bayes factor. The frequentist p-value is also shown. The null hypothesis, H_{0}
is that the effect δ = 0, and the alternative H_{1}: δ ≠ 0, just like a two-tailed *t* test.
You can use the sliders to vary the observed effect (Cohen's d), sample size (*n* per group) and the
prior on δ.

The **prior **on the effect is a scaled unit-information prior. The black, and red circle on
the curves represents the likelihood of 0 under the prior and posterior. Their likelihood ratio is the
Savage-Dickey density ratio, which I use here to compute the Bayes factor. The
** p-value** is the traditional

Have any suggestion? Or found any bugs? Send them to me, my contact info can be found here.