Confirmation, disconfirmation, and information in hypothesis testing.

Abstract

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, howevei; 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.A substantial proportion of the psychological literature on hypothesis testing has dealt with issues of confirmation and disconfirmation.Interest in this topic was spurred by the research findings of Wason (e.g., 1960Wason (e.g., ,1968) ) and by writings in the philosophy of science (e.g., Lakatos, 1970; Platt, 1964;Popper, 1959Popper, , 1972)), which related hypothesis testing to the pursuit of scientific inquiry.Much of the work in this area, both empirical and theoretical, stresses the importance of disconfirmation in learning and reasoning.In contrast, human reasoning is often said to be prone to a "confirmation bias" that hinders effective learning.However, confirmation bias has meant different things to different investigators, as Fischhoff and Beyth-Marom point out in a recent review (1983).For example, researchers studying the perception of correlations have proposed that people are overly influenced by the co-occurrence of two events and insufficiently influenced by instances in which one event occurs without the other (e.g.,

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• University of Chicago US

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