Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. To aid the development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (1) in the translation of real-world goals to goals on a particular set of training data, (2) in the…

Risky decisions are often characterized by (a) imprecision about consequences and their likelihoods that can be reduced by information collection, and by (b) unavoidable background risk. This article addresses both aspects by eliciting risk attitude, prudence, and temperance in decisions from description and decisions from experience. The results…

Previous research has shown that many choice biases are attenuated when short-run decisions are reframed to the long run. However, this literature has been limited to description-based choice tasks in which possible outcomes and their probabilities are explicitly specified. A recent literature has emerged showing that many core results found using…

Individuals can partly recreate the "wisdom of crowds" within their own minds by combining nonredundant estimates they themselves have generated. Herzog and Hertwig (2009) showed that this accuracy gain could be boosted by urging people to actively think differently when generating a 2nd estimate ("dialectical bootstrapping").…

There is considerable research on the difficulties students have in conceptualising individual concepts of probability and statistics (see for example, Bryant & Nunes, 2012; Jones, 2005). The unit of work developed for the action research project described in this article is specifically designed to address some of these in order to help…

Recently, researchers have investigated differences in decision making based on description and experience. We address the issue of when experience-based judgments of probability are more accurate than are those based on description. If description is well understood ("transparent") and experience is misleading ("wicked"), it…

We present a theory of decision by sampling (DbS) in which, in contrast with traditional models, there are no underlying psychoeconomic scales. Instead, we assume that an attribute's subjective value is constructed from a series of binary, ordinal comparisons to a sample of attribute values drawn from memory and is its rank within the sample. We…

This textbook for teaching reasoning processes and decision making under conditions of uncertainty is written at an introductory level. Based on a translation of a Hebrew book, the text is intended for the general training of military and civilian personnel required to process, sort, and/or evaluate incomplete, unreliable information. Potential…

Too few researchers understand what statistical significance testing does and does not do, and consequently their results are misinterpreted. This Digest explains the concept of statistical significance testing and discusses the meaning of probabilities, the concept of statistical significance, arguments against significance testing,…

In this study we test the performance of some nonexperimental estimators of impacts applied to an educational intervention--reduction in class size--where achievement test scores were the outcome. We compare the nonexperimental estimates of the impacts to "true impact" estimates provided by a random-assignment design used to assess the…