Results 1  10
of
22
Updating Probabilities
, 2002
"... As examples such as the Monty Hall puzzle show, applying conditioning to update a probability distribution on a "naive space", which does not take into account the protocol used, can often lead to counterintuitive results. Here we examine why. A criterion known as CAR ("coarsening a ..."
Abstract

Cited by 53 (6 self)
 Add to MetaCart
As examples such as the Monty Hall puzzle show, applying conditioning to update a probability distribution on a "naive space", which does not take into account the protocol used, can often lead to counterintuitive results. Here we examine why. A criterion known as CAR ("coarsening at random") in the statistical literature characterizes when "naive" conditioning in a naive space works. We show that the CAR condition holds rather infrequently, and we provide a procedural characterization of it, by giving a randomized algorithm that generates all and only distributions for which CAR holds. This substantially extends previous characterizations of CAR. We also consider more generalized notions of update such as Jeffrey conditioning and minimizing relative entropy (MRE). We give a generalization of the CAR condition that characterizes when Jeffrey conditioning leads to appropriate answers, and show that there exist some very simple settings in which MRE essentially never gives the right results. This generalizes and interconnects previous results obtained in the literature on CAR and MRE.
Individuation, counting, and statistical inference: the role of frequency and whole object representations in judgment under uncertainty
 Journal of Experimental Psychology:General
, 1998
"... ..."
A logical approach to reasoning about uncertainty: a tutorial
 Discourse, Interaction, and Communication
, 1998
"... I consider a logical framework for modeling uncertainty, based on the use of possible worlds, that incorporates knowledge, probability, and time. This turns out to be a powerful approach for modeling many problems of interest. I show how it can be used to give insights into (among other things) seve ..."
Abstract

Cited by 12 (0 self)
 Add to MetaCart
I consider a logical framework for modeling uncertainty, based on the use of possible worlds, that incorporates knowledge, probability, and time. This turns out to be a powerful approach for modeling many problems of interest. I show how it can be used to give insights into (among other things) several wellknown puzzles.
Partitioneditcount: Naïve extensional reasoning in conditional probability judgment
 Journal of Experimental Psychology: General
, 2004
"... The authors provide evidence that people typically evaluate conditional probabilities by subjectively partitioning the sample space into n interchangeable events, editing out events that can be eliminated on the basis of conditioning information, counting remaining events, then reporting probabiliti ..."
Abstract

Cited by 11 (5 self)
 Add to MetaCart
The authors provide evidence that people typically evaluate conditional probabilities by subjectively partitioning the sample space into n interchangeable events, editing out events that can be eliminated on the basis of conditioning information, counting remaining events, then reporting probabilities as a ratio of the number of focal to total events. Participants ’ responses to conditional probability problems were influenced by irrelevant information (Study 1), small variations in problem wording (Study 2), and grouping of events (Study 3), as predicted by the partition–edit–count model. Informal protocol analysis also supports the authors ’ interpretation. A 4th study extends this account from situations where events are treated as interchangeable (chance and ignorance) to situations where participants have information they can use to distinguish among events (uncertainty). People are often called on to make judgments of conditional likelihood. For example, a patient might try to assess the likelihood of having a disease, given a positive test result; a litigant might try to estimate the odds of prevailing in court, given a piece of damning evidence. Over the last 30 years, psychologists have shown that people typically judge conditional probabilities using a
An Analysis of Kahneman
 Heuristics and Biases’, Science 185
, 2000
"... The study of intuitions and errors in judgment un~‘er umwtainty is complicated by several factors: discrepancies between acceptance and application of normative rules: effects of content on me application of rules; Sucratic hints that create intuitions while testing them; demand characteristics of w ..."
Abstract

Cited by 10 (0 self)
 Add to MetaCart
The study of intuitions and errors in judgment un~‘er umwtainty is complicated by several factors: discrepancies between acceptance and application of normative rules: effects of content on me application of rules; Sucratic hints that create intuitions while testing them; demand characteristics of withinsubject experiments; subjects ’ interpretations of experimental messages according to standard conversational rules. The positive analysis of a iudgmental error in terms of heuri.stics may be supplemented by a negative analysis, which seeks to explain why the correct rule is not intuitively compelling. A negative analysis of nonregressive prediction is outlined. Much of the recent literature on judgment and, inductive reasoning has been concerned with errors, biases and fallacies in a variety of mental tasks (see,
Evolutionary Versus Instrumental Goals: How Evolutionary Psychology Misconceives Human Rationality. Evolution and the psychology of thinking
, 2003
"... An important research tradition in the cognitive psychology of reasoningcalled the heuristics and biases approachhas firmly established that people’s responses often deviate from the performance considered normative on many reasoning tasks. For example, people assess probabilities incorrectly, t ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
An important research tradition in the cognitive psychology of reasoningcalled the heuristics and biases approachhas firmly established that people’s responses often deviate from the performance considered normative on many reasoning tasks. For example, people assess probabilities incorrectly, they display confirmation bias, they test hypotheses inefficiently, they violate the axioms of utility theory, they do not properly calibrate degrees of belief, they overproject their own opinions onto others, they display illogical framing effects, they uneconomically honor sunk costs, they allow prior knowledge to become implicated in deductive reasoning, and they display numerous other information processing biases (for summaries of the large literature, see
Principles and procedures of exploratory data analysis
 Psychological Methods
, 1997
"... Exploratory data analysis (EDA) is a wellestablished statistical tradition that provides conceptual and computational tools for discovering patterns to foster hypothesis development and refinement. These tools and attitudes complement the use of significance and hypothesis tests used in confirmator ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
Exploratory data analysis (EDA) is a wellestablished statistical tradition that provides conceptual and computational tools for discovering patterns to foster hypothesis development and refinement. These tools and attitudes complement the use of significance and hypothesis tests used in confirmatory data analysis (CDA). Although EDA complements rather than replaces CDA, use of CDA without EDA is seldom warranted. Even when wellspecified theories are held, EDA helps one interpret the results of CDA and may reveal unexpected or misleading patterns in the data. This article introduces the central heuristics and computational tools of EDA and contrasts it with CDA and exploratory statistics in general. EDA techniques are illustrated using previously published psychological data. Changes in statistical training and practice are recommended to incorporate these tools. The widespread availability of software for graphical data analysis and calls for increased use of exploratory data analysis (EDA) on epistemic grounds (e.g. Cohen, 1994) have increased the visibility of EDA. Nevertheless, few psychologists receive explicit training in the beliefs or procedures of this tradition. Huberty (1991) remarked that statistical texts are likely to give cursory references to common EDA techniques such as stemandleaf plots, box plots, or residual analysis and yet seldom integrate these techniques throughout a book. A survey of graduate training programs in psychology corroborates such an impression
How to confuse with statistics. The use and misuse of conditional probabilities
 Statistical Science
, 2005
"... Abstract. This article shows by various examples how consumers of statistical information may be confused when this information is presented in terms of conditional probabilities. It also shows how this confusion helps others to lie with statistics, and it suggests both confusion and lies can be exp ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
Abstract. This article shows by various examples how consumers of statistical information may be confused when this information is presented in terms of conditional probabilities. It also shows how this confusion helps others to lie with statistics, and it suggests both confusion and lies can be exposed by using alternative modes of conveying statistical information. Key words and phrases: Conditional probabilities, natural frequencies, heuristical reasoning. 1.
Assessing psychology students’ difficulties with conditional probability and bayesian reasoning
 In A. Rossman & B. Chance (Eds.), Proceedings of the Seventh International Conference on Teaching Statistics
, 2006
"... Conditional probability and Bayesian reasoning are important to psychology students because they are involved in the understanding of classical and Bayesian inference, regression and correlation, linear models, multivariate analysis and other statistical procedures that are often used in psychologic ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
Conditional probability and Bayesian reasoning are important to psychology students because they are involved in the understanding of classical and Bayesian inference, regression and correlation, linear models, multivariate analysis and other statistical procedures that are often used in psychological research. A study of previous literature showed that there is considerable research on this topic, but no comprehensive questionnaires have been developed to globally assess students ' understanding and misconceptions on these topics. At the University of Granada we started building a questionnaire, which takes into account the content of conditional probability taught in the Spanish universities to psychology students, as well as the biases and misconceptions described in the literature. In this work we will describe the process of developing the questionnaire and will report the results from a sample of 206 psychology students.
The Monty Hall Dilemma Revisited: Understanding the Interaction of Problem Definition and Decision Making
, 1999
"... We examine a logical decision problem, the "Monty Hall Dilemma," in which a large portion of sophisticated subjects appear to insist on an apparently wrong solution. Although a substantial literature examines the structure of this problem, we argue that the extant analyses have not recogni ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
We examine a logical decision problem, the "Monty Hall Dilemma," in which a large portion of sophisticated subjects appear to insist on an apparently wrong solution. Although a substantial literature examines the structure of this problem, we argue that the extant analyses have not recognized the constellation of cues that guide respondents' answers. We show that insight into subjects' decisions may be obtained by considering problems with similar surface structure to the Monty Hall Dilemma but which are common in environments that they routinely face. In particular, we consider the problem modeled as a game in which actors have possibly opposing interests, and as an environment with information provided by an objective source. We present experimental evidence showing that these comparisons help to explain subject responses.