Eric-Jan WagenmakersNeuroscience Symposium
The Neuroscience Symposia are organized weekly by the Netherlands Institute for Neuroscience. The presentations are given by researchers from the institute or by guest speakers. The title and content of the symposium is usually made known in the week prior to the presentation.
Guest speaker Christian Keysers
Colloquium room – Netherlands Institute for Neuroscience
4:00 pm – Bayesian inference with JASP
4:45 pm – Discussion and drinks
Have you ever had a negative finding (non-significant t-test), and wondered what conclusions you can draw? Most of us have been ‘brought up’ with the credo that if a t-test is non-significant, we have absence of evidence for an experimental effect, but that does not represent evidence of absence, i.e. we cannot say that there was no effect of our manipulation. This severely limits the inference we can draw in many of our experiments, and is the reason why null-effects are so hard to publish. We the advent of Bayesian statistics, this has changed. Using the Bayes factor, we can take the data from cases in which we have a non-significant effect, and determine if our data is too noisy to say anything, or whether we actually have solid evidence for the absence of an effect. This methodology was often too difficult to perform routine-wise, and still has not penetrated the neurosciences. Eric-Jan Wagenmakers, one of the world leading Bayesian statistician, has changed this state of affair, by creating a simple and powerful software that is freely available, and allows us to interpret many of our negative findings (jasp-stats.org). I have started to use that software for many of my negative findings, and has opened new ways to interpret our data. Today, I asked him to speak to the NIN, and give a talk targeted at scientists that no little about Bayesian statistics, and show us how to perform and interpret these Bayesian versions of T-tests and Anova’s. I think the talk will be of great interest to many of us, and I invite you all to come, despite the good weather.