Robust Inference Policies
This work addresses robustness in statistical inference for researchers, but it is incremental as it compares existing methods without introducing new techniques.
The study investigated how different inference procedures maintain reasonable belief values under increasing judgmental imprecision, finding that Bayesian methods are more powerful but also more error-prone, with a higher likelihood of strongly supporting both correct and incorrect hypotheses compared to an equal-weights linear model.
A series of monte carlo studies were performed to assess the extent to which different inference procedures robustly output reasonable belief values in the context of increasing levels of judgmental imprecision. It was found that, when compared to an equal-weights linear model, the Bayesian procedures are more likely to deduce strong support for a hypothesis. But, the Bayesian procedures are also more likely to strongly support the wrong hypothesis. Bayesian techniques are more powerful, but are also more error prone.