Sprecher
Beschreibung
When analyzing their data, researchers usually need to choose among a number of statistical models that are not diverse and flexible enough to adequately capture the data generation mechanism. In this situation, they often make the problem fit the tools by introducing auxiliary assumptions that are at best questionable and at worst indefensible. This talk will illustrate on past and ongoing projects how Bayesian hierarchical models can help to relax auxiliary assumptions through the explicit modeling of various sources of evidence and of uncertainty. It will also discuss how the flexibility in choosing a model that adequately captures many aspects of the data generating mechanism can help address the problem of overconfidence in statistical results and how its ability to avoid making the problem fit the tools can be a boon and a bane.