1987/04/01 by Joshua Klayman, Young-Won Ha · 11 citations
Arts and Humanities · Biochemistry, Genetics and Molecular Biology · Computer Science · Psychology · Mathematics · #Philosophy and History of Science #Biomedical Text Mining and Ontologies #Explainable Artificial Intelligence (XAI) #Psychology #Statistical hypothesis testing #Cognitive psychology #Social psychology #Statistics #Mathematics
paper · doi:10.1037/0033-295x.94.2.211
openalex publication_date 1987/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.