1. BACKGROUND The last twenty-five years have seen a large amount of psychological research in the area of behavioral decision theory. It followed the major breakthrough of decision theory that came with von Neumann and Morgenstern's Theory of Games and Economic Behavior in 1944. The key concepts are probability as a measure of uncertainty and utility as a measure of value and risk. The theory prescribes, given some behavioral axioms, that alternatives should be ranked in accordance with their expected utilities. Psychologists became interested in studying how people's decision behavior agreed with what was prescribed by the theory. Three broad areas for research developed, i. e. , research relating to each of the two concepts of probability and utility, and research relating to the interaction of the two in decision stituations. The papers in this book have been selected to illustrate various aspects of how the concept of probability has been used in psychological ex perimentation. The early experiments were generated, as mentioned above, by an interest among psychologists to see how people evaluate uncertainty and quantify it in probabilistic terms. Many of these experiments set out to evaluate subjects' estimates of relative frequencies; these were situations where one had access to 'objective' answers. In the 1960's psychologists changed the focus of their studies to how people revise probabilistic judgments when they receive new information. In recent years there has been a growing interest in the cognitive processes by which people express their judgment in probabilistic terms.

We confess that the first part of our title is somewhat of a misnomer. Bayesian reasoning is a normative approach to probabilistic belief revision and, as such, it is in need of no improvement. Rather, it is the typical individual whose reasoning and judgments often fall short of the Bayesian ideal who is the focus of improvement. What have we learnt from over a half-century of research and theory on this topic that could explain why people are often non-Bayesian? Can Bayesian reasoning be facilitated, and if so why? These are the questions that motivate this Frontiers in Psychology Research Topic. Bayes' theorem, named after English statistician, philosopher, and Presbyterian minister, Thomas Bayes, offers a method for updating one’s prior probability of an hypothesis H on the basis of new data D such that P(H|D) = P(D|H)P(H)/P(D). The first wave of psychological research, pioneered by Ward Edwards, revealed that people were overly conservative in updating their posterior probabilities (i.e., P(D|H)). A second wave, spearheaded by Daniel Kahneman and Amos Tversky, showed that people often ignored prior probabilities or base rates, where the priors had a frequentist interpretation, and hence were not Bayesians at all. In the 1990s, a third wave of research spurred by Leda Cosmides and John Tooby and by Gerd Gigerenzer and Ulrich Hoffrage showed that people can reason more like a Bayesian if only the information provided takes the form of (non-relativized) natural frequencies. Although Kahneman and Tversky had already noted the advantages of frequency representations, it was the third wave scholars who pushed the prescriptive agenda, arguing that there are feasible and effective methods for improving belief revision. Most scholars now agree that natural frequency representations do facilitate Bayesian reasoning. However, they do not agree on why this is so. The original third wave scholars favor an evolutionary account that posits human brain adaptation to natural frequency processing. But almost as soon as this view was proposed, other scholars challenged it, arguing that such evolutionary assumptions were not needed. The dominant opposing view has been that the benefit of natural frequencies is mainly due to the fact that such representations make the nested set relations perfectly transparent. Thus, people can more easily see what information they need to focus on and how to simply combine it. This Research Topic aims to take stock of where we are at present. Are we in a proto-fourth wave? If so, does it offer a synthesis of recent theoretical disagreements? The second part of the title orients the reader to the two main subtopics: what works and why? In terms of the first subtopic, we seek contributions that advance understanding of how to improve people’s abilities to revise their beliefs and to integrate probabilistic information effectively. The second subtopic centers on explaining why methods that improve non-Bayesian reasoning work as well as they do. In addressing that issue, we welcome both critical analyses of existing theories as well as fresh perspectives. For both subtopics, we welcome the full range of manuscript types.

The book explores the variety of meanings of contextuality across different disciplines, with the emphasis on quantum physics and on psychology.

Even if you have no background in experimentation, this clear, straightforward book can help you design, execute, interpret, and report simple experiments in psychology. David W. Martin's unique blend of informality, humor, and solid scholarship have made this concise book a popular choice for methods courses in psychology. Doing Psychology Experiments guides you through the experimentation process in an easy-to-follow, step-by-step manner. Decision-making aspects of research are emphasized, and the logic behind research procedures is fully explained.

Essential Statistical Methods for Medical Statistics presents only key contributions which have been selected from the volume in the Handbook of Statistics: Medical Statistics, Volume 27 (2009). While the use of statistics in these fields has a long and rich history, the explosive growth of science in general, and of clinical and epidemiological sciences in particular, has led to the development of new methods and innovative adaptations of standard methods. This volume is appropriately focused for individuals working in these fields. Contributors are internationally renowned experts in their respective areas. - Contributors are internationally renowned experts in their respective areas - Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research - Methods for assessing Biomarkers, analysis of competing risks - Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs - Structural equations modelling and longitudinal data analysis

Until recently, most psychological research was conducted using subject samples in close proximity to the investigators--namely university undergraduates. In recent years, however, it has become possible to test people from all over the world by placing experiments on the internet. The number of people using the internet for this purpose is likely to become the main venue for subject pools in coming years. As such, learning about experiments on the internet will be of vital interest to all research psychologists. Psychological Experiments on the Internet is divided into three sections. Section I discusses the history of web experimentation, as well as the advantages, disadvantages, and validity of web-based psychological research. Section II discusses examples of web-based experiments on individual differences and cross-cultural studies. Section III provides readers with the necessary information and techniques for utilizing the internet in their own research designs. Innovative topic that will capture the imagination of many readers Includes examples of actual web based experiments

"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com

Methods in Psychological Research introduces students to the rich world of research in psychology through student-friendly writing, compelling real-world examples, and frequent opportunities for practice. Using a relaxed yet supportive tone that eases student anxiety, the authors present a mixture of conceptual and practical discussions, and spark reader interest in research by covering meaningful topics that resonate with today’s students. In-text features like Conceptual Exercises, FYI sections, and FAQ sections with accompanying visual cues support learning throughout the research experience. The Fourth Edition equips students with the tools they need to understand research concepts, conduct their own experiments, and present their findings.