Strategies for hypothesis testing in scientific investigation and everyday reasoning have interested both psychologists and philosophers. A number of these scholars stress the importance of disconnrmation in reasoning and suggest that people are instead prone to a general deleterious "confirmation bias." In particular, it is suggested that people tend to test those cases that have the best chance of verifying current beliefs rather than those that have the best chance of falsifying them. We show, however; that many phenomena labeled "confirmation bias" are better understood in terms of a general positive test strategy. With this strategy, there is a tendency to test cases that are expected (or known) to have the property of interest rather than those expected (or known) to lack that property. This strategy is not equivalent to confirmation bias in the first sense; we show that the positive test strategy can be a very good heuristic for determining the truth or falsity of a hypothesis under realistic conditions. It can, however, lead to systematic errors or inefficiencies. The appropriateness of human hypothesis-testing strategies and prescriptions about optimal strategies must be understood in terms of the interaction between the strategy and the task at hand.

We present a framework for the rational analysis of elemental causal induction – learning about the existence of a relationship between a single cause and effect – based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relationship might be. We show that two leading rational models of elemental causal induction, ∆P and causal power, both estimate causal strength, and introduce a new rational model, causal support, that assesses causal structure. Causal support predicts several key phenomena of causal induction that cannot be accounted for by other rational models, which we explore through a series of experiments. These phenomena include the complex interaction between ∆P and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal learning from rates. Causal support also provides a better account of a number of existing datasets than either ∆P or causal power.

Recent research on contingency judgment indicates that the judged predictiveness of a cue is dependent on the predictive strengths of other cues. Two classes of models correctly predict such cue interaction: associative models and statistical models. However, these models differ in their predictions about the effect of trial order on cue interaction. In five experiments reported here, college students viewed trial-by-trial data regarding several medical symptoms and a disease, judging the predictive strength of each symptom with respect to the disease. The results indicate that trial order influences the manner in which cues interact, but that neither the associative nor the statistical models can fully account for the data pattern. A possible variation of an associative account is discussed. The ability to detect predictive relationships among envi-ronmental events grants humans and other animals a distinct benefit. Therefore, the mechanisms underlying this ability are of considerable interest. Recent research with humans on judgments of contingencies has shed light on these mecha-nisms. It has suggested two classes of theoretical models that

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When participants assess the relationship between two variables, each with levels of presence and absence, the two most robust phenomena are that: (a) observing the joint presence of the variables has the largest impact on judgment and observing joint absence has the smallest impact, and (b) participants’ prior beliefs about the variables ’ relationship influence judgment. Both phenomena represent departures from the traditional normative model (the phi coefficient or related measures) and have therefore been interpreted as systematic errors. However, both phenomena are consistent with a Bayesian approach to the task. From a Bayesian perspective: (a) joint presence is normatively more informative than joint absence if the presence of variables is rarer than their absence, and (b) failing to incorporate prior beliefs is a normative error. Empirical evidence is reported showing that joint absence is seen as more informative than joint presence when it is clear that absence of the variables, rather than their presence, is rare.

Practitioners and teachers should be able to justify their chosen techniques by taking into account research results: This is evidence-based practice (EBP). We argue that, specifically, statistical practice and statistics education should be guided by evidence, and we propose statistical cognition (SC) as an integration of theory, research, and application to support EBP. SC is an interdisciplinary research field, and a way of thinking. We identify three facets of SC—normative, descriptive, and prescriptive— and discuss their mutual influences. Unfortunately, the three components are studied by somewhat separate groups of scholars, who publish in different journals. These separations impede the implementation of EBP. SC, however, integrates the facets and provides a basis for EBP in statistical practice and education.

this paper. Correspondence should be addressed to M. J. Dennis, Madsen Center, Augustana College, 2001 S. Summit Ave., Sioux Falls, SD 57197 (e-mail: dennis@inst.augie . edu)

Some selected interpretations of Pearson's correlation coefficient are considered. Correlation may be interpreted as a measure of closeness to identity of the standardized variables. This interpretation has a psychological appeal in showing that perfect covariation means identity up to positive linearity. It is well known that j r j is the geometric mean of the two slopes of the regression lines. In the 2 \Theta 2 case, each slope reduces to the difference between two conditional probabilities so that j r j equals the geometric mean of these two differences. For bivariate distributions with equal marginals, that satisfy some additional conditions, a nonnegative r conveys the probability that the paired values of the two variables are identical by descent. This interpretation is inspired by the rationale of the genetic coefficient of inbreeding. 1. Introduction 1.1 A Universal Measure with Multiple Interpretations Pearson's product-moment correlation coefficient, r , is ubiquitously us...

this article focuses on an experiential approach to strategies for change. It begins with a short exercise for the reader. (This approach could be easily extended to a classroom situation). Following this, two strategies are discussed. The first is the one most commonly used; the second, called the Delta Technique, is developed from a substantial body of published research. One aspect of the experiential approach is that you are asked to solve a problem before you are given n technique for solving it. Thus, I will ask you to solve the COMPU-HEART case before I present the Delta Technique. Another aspect of the experiential approach relates to your orientation as a reader. It is suggested that the Delta Technique is a proven method -- for others. But it may differ from your current approach to change. How would you react to this information? There are other possibilities, but check the item closest to your expected reaction: (A) I will disagree with the Delta Technique. This article will not provide enough information to convince me. (B) I will decide that it was really a communication problem -- that I already use the Delta Technique but have different words for it. (C) I will feel that I had read an exciting article and agree that the Delta Technique is excellent. (D) I will take action by experimenting with the Delta Technique. Alternative D, to experiment, creates stress. Yet D has the most value to you and your organization. Those selecting A will feel bad, Bs will feel O.K., and Cs will feel good: But D provides an opportunity to benefit from this article. (If you would like to compare your responses with others, a sample of MBAs from two organizational behavior courses at Wharton answered this question before reading this article. Of the 23 respondents, 65% sele...

Integration of contingency information underlies many cognitive tasks including causal, covariational, and probability judgments. The authors'feature-analytic approach was used to account for the findings that people differentially weight specific types of conjunctive information in causal (Experiment 1) and noncausal (Experiment 2) contingency judgments. These findings were explained in terms of positive-test and sufficiency-test biases, which were found in both judgment domains. The same biases, however, were not observed in normative conditional-probability judgments (Experiment 3). The authors argue that this discrepancy is owing to the differential clarity of normative criteria in these domains. Much of human learning and inferential thinking depends on the integration of contingency information. To test hypotheses and revise beliefs; to explain past events and predict future ones; to establish categories, form stereotypes, and develop impressions of others, humans integrate a vast amount of information about interevent contingencies. In short, the ability to discriminate contingencies in the physical