An Introduction to Jeffreys’s Bayes Factors With the SumStats Module in JASP: Part 1 | Bayes methods from:

In this blog post we elaborate on the ideas behind Harold Jeffreys’s Bayes factor and illustrate this test with the Summary Statistics module in JASP.

In a previous blog post we discussed the estimation problem, where the goal was to infer, from the observed data, the magnitude of the population effect. Before studying the size of an effect, however, we arguably first need to investigate whether an effect actually exists. Here we address the existence problem with a hypothesis test and we emphasize the difference between testing and estimation.

The outline of this blog post is as follows: Firstly, we discuss a hypothesis proposed in a recent study relating fungal infections to Alzheimer’s disease. This hypothesis is then operationalized within a statistical model, and we discuss Bayesian model learning in general, before we return to the Alzheimer’s example. This is followed by a comparison of the Bayes factor to other methods of inference, and the blog post concludes with a short summary.

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