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    Wright, M. Keith 2018. Intelligent Systems. p. 89.

    Wright, M. Keith 2017. Towards a New Model for Causal Reasoning in Expert Systems. International Journal of Knowledge-Based Organizations, Vol. 7, Issue. 1, p. 32.

    Rensink, Ronald A. 2017. The nature of correlation perception in scatterplots. Psychonomic Bulletin & Review, Vol. 24, Issue. 3, p. 776.

    Tran, Dung 2016. Statistical Association: Alignment of Current U.S. High School Textbooks with the Common Core State Standards for Mathematics. School Science and Mathematics, Vol. 116, Issue. 5, p. 286.

    Pleskac, Timothy J. 2015. The Wiley Blackwell Handbook of Judgment and Decision Making. p. 629.

    Schmidt, J. Michael and De Georgia, Michael 2014. Multimodality Monitoring: Informatics, Integration Data Display and Analysis. Neurocritical Care, Vol. 21, Issue. S2, p. 229.

    Bathke, Allen W. Morton, Richard M. Notbohm, Matthew Zhang, Tianming and Cahan, Steven 2014. Objective estimation versus subjective perceptions of earnings patterns and post-earnings-announcement drift. Accounting & Finance, Vol. 54, Issue. 2, p. 305.

    Meyer, Matthias Grisar, Cathérine and Kuhnert, Felix 2011. The impact of biases on simulation-based risk aggregation: modeling cognitive influences on risk assessment. Journal of Management Control, Vol. 22, Issue. 1, p. 79.

    Alfano, Mark 2011. Explaining Away Intuitions About Traits: Why Virtue Ethics Seems Plausible (Even if it Isn’t). Review of Philosophy and Psychology, Vol. 2, Issue. 1, p. 121.

    Soffer, Shira and Kareev, Yaakov 2011. The effects of problem content and scientific background on information search and the assessment and valuation of correlations. Memory & Cognition, Vol. 39, Issue. 1, p. 107.

    KELLY, KHIM 2010. Accuracy of Relative Weights on Multiple Leading Performance Measures: Effects on Managerial Performance and Knowledge*. Contemporary Accounting Research, Vol. 27, Issue. 2, p. 577.

    Green, Kesten C. and Armstrong, J. Scott 2007. The Ombudsman: Value of Expertise for Forecasting Decisions in Conflicts. Interfaces, Vol. 37, Issue. 3, p. 287.

    Doherty, Michael E. Anderson, Richard B. Angott, Andrea M. and Klopfer, Dale S. 2007. The perception of scatterplots. Perception & Psychophysics, Vol. 69, Issue. 7, p. 1261.

    Syed, Zeeshan Guttag, John and Stultz, Collin 2007. Clustering and Symbolic Analysis of Cardiovascular Signals: Discovery and Visualization of Medically Relevant Patterns in Long-Term Data Using Limited Prior Knowledge. EURASIP Journal on Advances in Signal Processing, Vol. 2007, Issue. 1,

    Wartenberg, Katja Elfriede Schmidt, J Michael and Krieger, Derk W 2006. Future of brain support: multimodality monitoring. Future Neurology, Vol. 1, Issue. 4, p. 465.

    Mishra, Arul and Nayakankuppam, Dhananjay 2006. Consistency and Validity Issues in Consumer Judgments. Journal of Consumer Research, Vol. 33, Issue. 3, p. 291.

    Caplan, Bryan 2006. How do voters form positive economic beliefs? Evidence from the Survey of Americans and Economists on the Economy. Public Choice, Vol. 128, Issue. 3-4, p. 367.

    Sabini, John and Silver, Maury 2005. Lack of Character? Situationism Critiqued. Ethics, Vol. 115, Issue. 3, p. 535.

    Todorov, Alexander 2003. Cognitive procedures for correcting proxy-response biases in surveys. Applied Cognitive Psychology, Vol. 17, Issue. 2, p. 215.

    Baker, Geri Wilkerson, Keith Mcgahan, Joseph Mccown, Bill and Williamson, David 2002. Judgments about Length, Weight, and width Covariation as a Function of Intelligence and Depth of Processing in Elementary School Children: A Preliminary Study. Psychological Reports, Vol. 90, Issue. 3_part_2, p. 1109.

  • Print publication year: 1982
  • Online publication date: May 2013

15 - Informal covariation assessment: Data-based versus theory-based judgments


The flow of social experience frequently challenges us to recognize empirical covariations. Sometimes, these covariations are merely another test of our powers of observation and are of no immediate practical concern to us. At other times – for example, when those covariations involve early symptoms of problems and later manifestations, or behavioral strategies employed and outcomes obtained, or relatively overt characteristics of people or situations and relatively covert ones – such detection abilities may help to determine our success in adapting to the demands of everyday social life. More generally, covariation detection will play a large role in our continuing struggle as “intuitive scientists” (see Nisbett & Ross, 1980; Ross, 1977, 1978) to evaluate and update the hypotheses we hold about ourselves, our peers, and our society. An obvious question therefore presents itself: How proficient are we, as laypeople, at assessing the empirical covariations presented by experiential evidence?

Before proceeding to discuss past or present research, we should note that everyday observation provides a great deal of relevant evidence; and it hints that the answer to the proficiency question is apt to be far from a simple one. On the one hand, both the generally adaptive nature of social behavior and the generally harmonious quality of social interaction leave little doubt that the participants in our culture possess many profound insights about behavioral causes and consequences.

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Judgment under Uncertainty
  • Online ISBN: 9780511809477
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