Psychonomic Bulletin & Review

, Volume 14, Issue 6, pp 1147–1152 | Cite as

Intuitivet tests: Lay use of statistical information

  • Natalie A. Obrecht
  • Gretchen B. Chapman
  • Rochel Gelman
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Abstract

Normatively, a statistical pairwise comparison is a function of the mean, standard deviation (SD), and sample size of the data. In our experiment, 203 undergraduates compared product pairs and judged their confidence that one product was better than the other. We experimentally manipulated (within subjects) the average product ratings, the number of raters (sample size), and theSD of the ratings. Each factor had two levels selected, so that the same change in statistical power resulted from moving from the low to the high level. We also manipulated (between subjects) whether subjects were given only the product rating data as summarized in a statistical format or the summaries plus the raw ratings. Subjects gave the most weight to mean product ratings, less weight to sample size, and very little weight toSD. Providing subjects with raw data did not increase their use of sample size andSD, as predicted.

References

  1. Frederick, S. (2005). Cognitive reflection and decision making.Journal of Economic Perspectives,19, 25–42.CrossRefGoogle Scholar
  2. Gallistel, C. R., &Gelman, R. (2005). Mathematical cognition. In K. Holyoak & R. Morrison (Eds.),The Cambridge handbook of thinking and reasoning (pp. 559–588). Cambridge: Cambridge University Press.Google Scholar
  3. Gigerenzer, G. (2000).Adaptive thinking: Rationality in the real world. New York: Oxford University Press.Google Scholar
  4. Gigerenzer, G., &Edwards, A. (2003). Simple tools for understanding risks: From innumeracy to insight.British Medical Journal,327, 741–744.CrossRefPubMedGoogle Scholar
  5. Gigerenzer, G., &Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: Frequency formats.Psychological Review,102, 684–704.CrossRefGoogle Scholar
  6. Glover, S., &Dixon, P. (2004). Likelihood ratios: A simple and flexible statistic for empirical psychologists.Psychonomic Bulletin & Review,11, 791–806.CrossRefGoogle Scholar
  7. Gottlieb, D., Weiss, T., &Chapman, G. B. (2007). The format in which uncertainty information is presented affects decision biases.Psychological Science,18, 240–246.CrossRefPubMedGoogle Scholar
  8. Hartnett, P. M., &Gelman, R. (1998). Early understandings of numbers: Paths or barriers to the construction of new understandings?Learning & Instruction,8, 341–374.CrossRefGoogle Scholar
  9. Hertwig, R., Barron, G., Weber, E., &Erev, I. (2004). Decisions from experience and the effect of rare events in risky choice.Psychological Science,15, 534–539.CrossRefPubMedGoogle Scholar
  10. Hertwig, R., &Gigerenzer, G. (1999). The “conjunction fallacy” revisited: How intelligent inferences look like reasoning errors.Journal of Behavioral Decision Making,12, 275–305.CrossRefGoogle Scholar
  11. Hoffrage, U., &Gigerenzer, G. (1998). Using natural frequencies to improve diagnostic inferences.Academic Medicine,73, 538–540.CrossRefPubMedGoogle Scholar
  12. Hoffrage, U., Lindsey, S., Hertwig, R., &Gigerenzer, G. (2000). Communicating statistical information.Science,290, 2261–2262.CrossRefPubMedGoogle Scholar
  13. Kahneman, D., &Tversky, A. (1972). Subjective probability: A judgment of representativeness.Cognitive Psychology,3, 430–454.CrossRefGoogle Scholar
  14. Lenth, R. V. (2006). Java applets for power and sample size [Computer software]. Retrieved January 2006 from www.stat.uiowa. edu/≈rlenth/Power.Google Scholar
  15. Nisbett, R. E., Krantz, D. H., Jepson, C., &Kunda, Z. (1983). The use of statistical heuristics in everyday inductive reasoning.Psychological Review,90, 339–363.CrossRefGoogle Scholar
  16. Obrecht, N. A., & Chapman, G. B. (2006, November).Intuitive t tests: Lay use of statistical information. Poster session presented at the Psychonomic Society Annual Meeting, Houston, TX.Google Scholar
  17. Obrecht, N. A., Chapman, G. B., & Gelman, R. (2006, November).Statistical reasoning is influenced by serial presentation of information. Paper presented at the annual meeting of the Society for Judgment and Decision Making, Houston.Google Scholar
  18. Sedlmeier, P., &Gigerenzer, G. (1997). Intuitions about sample size: The empirical law of large numbers.Journal of Behavioral Decision Making,10, 33–51.CrossRefGoogle Scholar
  19. Sedlmeier, P., &Gigerenzer, G. (2001). Teaching Bayesian reasoning in less than two hours.Journal of Experimental Psychology,130, 380–400.PubMedGoogle Scholar
  20. Tversky, A., &Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases.Science,185, 1124–1131.CrossRefPubMedGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2007

Authors and Affiliations

  • Natalie A. Obrecht
    • 1
  • Gretchen B. Chapman
    • 1
  • Rochel Gelman
    • 1
  1. 1.Psychology DepartmentRutgers UniversityPiscataway

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