The use of statistical significance and p-values has become a matter substantial controversy in various fields using methods. This gone as far some journals banning the indicators for significance, or even any reports p-values, and, one case, mention confidence intervals. I discuss three issues that have led to these often-heated debates. First, argue many cases, do not answer questions primary...

A proper representation of the uncertainty involved in a prediction is an important prerequisite for the acceptance of machine learning and decision support technology in safety-critical application domains such as medical diagnosis. Despite the existence of various probabilistic approaches in these fields, there is arguably no method that is able to distinguish between two very different sourc...

This paper develops a methodology for uncertainty quantification and model validation in the presence of epistemic uncertainty due to sparse and imprecise data. Three types of epistemic uncertainty regarding input random variables – interval data, sparse point data, and probability distributions with parameter uncertainty – are considered. When the model inputs are described using sparse point ...

The performance of a multidisciplinary system is inevitably affected by various sources of uncertainties, usually categorized as aleatory (e.g. input variability) or epistemic (e.g. model uncertainty) uncertainty. In the framework of design under uncertainty, all sources of uncertainties should be aggregated to assess the uncertainty of system quantities of interest (QOIs). In a multidisciplina...

Uncertainty in regression can be efficiently and effectively communicated using the visual properties of statistical objects in a regression display. Altering the “visual weight” of lines and shapes to depict the quality of information represented clearly communicates statistical confidence even when readers are unfamiliar with the formal and abstract definitions of statistical uncertainty. Her...