# Bayesian Spectacles for Everyone!

On particular occasions I find myself almost indifferent about the distinction between frequentist and Bayesian statistics. Usually this happens on weekends, after a few drinks, when my son has fallen asleep, and I am watching my favorite TV show. In those moments of weakness, I find myself thinking “Why all the fuss — often the two paradigms lead to the same conclusion” or “Why shouldn’t researchers present the outcomes from both paradigms? Is it really so harmful to present the outcome of frequentist statistics?” or “It may benefit students to be taught some frequentist statistics at some point in their education”. When this mood takes me I immediately turn off the TV, jump up from the couch, run to my computer, and start writing my JASP blog posts. After all, JASP aims to be statistically inclusive and facilitate both frequentist and Bayesian analysis.

However, on other occasions –on weekdays, when sober, and focusing on work– I am overcome by an intense desire to demonstrate that a Bayesian world is a better world: more sensible, more flexible, and much more elegant. This “Bayes is best” message is arguably not appropriate for the JASP blog, and this is why we (the JASP team, with special credit to Alexandra Sarafoglou, Tim Draws, Alexander Etz, and Dora Matzke) have created a new blog, “Bayesian Spectacles“.

This is the mission statement from the Bayesian Spectacles crew:

“Bayesian methods provide a unifying framework for all of statistics, ranging from the test of simple hypotheses and the estimation of parameters to decision making using loss functions and the model-averaged prediction of key outcome variables. Bayesian methods are principled in the sense that they adhere to the laws of probability theory, meaning that all uncertainty is taken into account and that no contradictory statements can occur. At the same time, Bayesian methods are unparalleled in their flexibility, and uniquely allow researchers to make statements about the relative evidence for hypotheses and parameter values – and to seamlessly update those statements as data accumulate.

We wish to improve current research practices and consequently our goal is to promote Bayesian methods and facilitate their use in concrete applications. Through this blog we hope to deliver Bayesianstories and ideas that are fun, interesting, and educational.”

The inaugural blog post was inspired by a discussion on the paper “Redefine Statistical Significance“. The post features trolls, herrings, and Lucifer. Who said statistics is boring?