Klayman and Ha examine the role of confirmation and disconfirmation in hypothesis testing, challenging the common view that people are prone to confirmation bias. They propose that a general positive test strategy, where people test cases expected to have the target property, is more accurate than confirmation bias. This strategy can be effective in many situations but may lead to systematic errors. The article discusses how confirmation and disconfirmation are related to hypothesis testing in scientific inquiry and everyday reasoning. It highlights the importance of disconfirmation in learning and reasoning, and the tendency of people to be influenced by confirmation bias. The authors argue that the positive test strategy is a good heuristic for determining the truth or falsity of a hypothesis under realistic conditions, but can lead to inefficiencies. The article also discusses the logic of ambiguous versus conclusive events, and the importance of testing cases that are most likely to prove a hypothesis wrong. It concludes that the positive test strategy may be a reasonable way to test a hypothesis in many situations, even though people may not be aware of the task conditions that favor or disfavor its use. The article also discusses the role of information in target tests and the importance of testing cases known to be targets rather than those known to be nontargets. It concludes that in probabilistic environments, the positive test strategy remains a useful heuristic, even though it may not always be optimal. The authors argue that the positive test strategy is a general heuristic that can be effective in many situations, but may not always be the best choice.Klayman and Ha examine the role of confirmation and disconfirmation in hypothesis testing, challenging the common view that people are prone to confirmation bias. They propose that a general positive test strategy, where people test cases expected to have the target property, is more accurate than confirmation bias. This strategy can be effective in many situations but may lead to systematic errors. The article discusses how confirmation and disconfirmation are related to hypothesis testing in scientific inquiry and everyday reasoning. It highlights the importance of disconfirmation in learning and reasoning, and the tendency of people to be influenced by confirmation bias. The authors argue that the positive test strategy is a good heuristic for determining the truth or falsity of a hypothesis under realistic conditions, but can lead to inefficiencies. The article also discusses the logic of ambiguous versus conclusive events, and the importance of testing cases that are most likely to prove a hypothesis wrong. It concludes that the positive test strategy may be a reasonable way to test a hypothesis in many situations, even though people may not be aware of the task conditions that favor or disfavor its use. The article also discusses the role of information in target tests and the importance of testing cases known to be targets rather than those known to be nontargets. It concludes that in probabilistic environments, the positive test strategy remains a useful heuristic, even though it may not always be optimal. The authors argue that the positive test strategy is a general heuristic that can be effective in many situations, but may not always be the best choice.