this I thought I’d branch this off from the other topics asking specific questions about fitting shifted_normal distributions (i.e. here)
A related topic is: what is current best practice for dealing with outliers? If I understand these models correctly, any reaction times that are shorter than the fitted “shift” parameter are impossible. In my current dataset, there are a few very short RTs (<50ms) which are very likely to be random button presses. For the time being, I’m excluding all RTs falling below the 1% quantile, or above the 99% quantile.
Does anybody have any better advice? I was wondering if it would be a good idea to fit a mixture of distributions, for example, on any given trials there is a small chance that the RT will be drawn from a uniform(0, xmax) distribution for some value xmax.
Has anybody had much success with this approach?