Just two steps are needed to go from computing p-values to computing Bayes factors. This post explains both steps and introduces Hyp-Chart, the missing link we arrive at if we take only the first step. Hyp-Chart is a graph that shows how well the data fit the null vs. every possible alternative hypothesis [1]. Hyp-Chart…

# Category: On Bayesian Stats

## [78a] If you think p-values are problematic, wait until you understand Bayes Factors

Would raising the minimum wage by $4 lead to greater unemployment? Milton, a Chicago economist, has a theory (supply and demand) that says so. Milton believes the causal effect is anywhere between 1% and 10%. After the minimum wage increase of $4, unemployment goes up 1%. Milton feels bad about the unemployed but good about…

## [42] Accepting the Null: Where to Draw the Line?

We typically ask if an effect exists. But sometimes we want to ask if it does not. For example, how many of the "failed" replications in the recent reproducibility project published in Science (.pdf) suggest the absence of an effect? Data have noise, so we can never say 'the effect is exactly zero.' We can…

## [35] The Default Bayesian Test is Prejudiced Against Small Effects

When considering any statistical tool I think it is useful to answer the following two practical questions: 1. "Does it give reasonable answers in realistic circumstances?" 2. "Does it answer a question I am interested in?" In this post I explain why, for me, when it comes to the default Bayesian test that's starting to…

## [13] Posterior-Hacking

Many believe that while p-hacking invalidates p-values, it does not invalidate Bayesian inference. Many are wrong. This blog post presents two examples from my new "Posterior-Hacking" (SSRN) paper showing selective reporting invalidates Bayesian inference as much as it invalidates p-values. Example 1. Chronological Rejuvenation experiment In "False-Positive Psychology" (SSRN), Joe, Leif and I run experiments to demonstrate how easy…