3/9/2023 0 Comments Shifted function![]() Effects can occur in the tails of the distributions too: for instance a particular intervention could have an effect only in animals with a certain hormonal level at baseline a drug could help participants with severe symptoms, but not others with milder symptoms… Because effects are not necessarily homogenous among participants, it is useful to have appropriate tools at hand, to determine how, and by how much, two distributions differ. In addition, there is no reason a priori to assume that two distributions differ only in the location of the bulk of the observations. the t-test is sufficient to detect changes in location.Īs we saw previously, t-tests on means are not robust.the typical observation in each distribution can be summarised by the mean.the distributions differ only in central tendency, not in other aspects.This standard procedure makes very strong assumptions: This is most of the time achieved using a t-test on means. In neuroscience & psychology, group comparison is usually an exercise that involves comparing two typical observations. And a Bayesian shift function is now available! The hierarchical shift function provides a powerful alternative to the t-test. UPDATE: The shift function and its cousin the difference asymmetry function are described in a review article with many examples. Matlab code is described in another post. The R code for the 2013 percentile bootstrap version of the shift function was also covered here and here. The R code for this post is available on github, and is based on Rand Wilcox’s WRS R package, with extra visualisation functions written using ggplot2.
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