Gerd Gigerenzer's article challenges the traditional view of cognitive biases in probabilistic reasoning, arguing that many so-called "errors" are not violations of probability theory but rather result from conceptual misunderstandings. He critiques the "heuristics and biases" framework, which has dominated social psychology and related fields, for its narrow normative standards and failure to recognize fundamental distinctions in probability theory, such as single-case versus frequency reasoning. Gigerenzer suggests that by applying these distinctions, apparent "errors" can be eliminated, reappear, or even invert. He argues that intuitive reasoning is highly sensitive to these distinctions, leading to a revised understanding of judgment under uncertainty. The article discusses several cognitive illusions, including overconfidence bias, the conjunction fallacy, and the base-rate fallacy. Gigerenzer shows that these are not necessarily violations of probability theory but rather result from the way people interpret single-event probabilities versus long-run frequencies. He provides examples where rephrasing problems in terms of frequencies rather than single events leads to more accurate judgments, demonstrating that the "errors" identified by the "heuristics and biases" program are not always flawed reasoning but can be explained by a more accurate understanding of probability. Gigerenzer also highlights the importance of random sampling in probabilistic reasoning, arguing that many studies fail to account for this crucial distinction. He shows that when problems are rephrased in terms of frequencies, subjects' judgments align more closely with Bayesian predictions, indicating that the "errors" identified in the "heuristics and biases" framework may be due to a misunderstanding of probability theory. Overall, Gigerenzer's work challenges the traditional view of cognitive biases and offers a more nuanced understanding of judgment under uncertainty.Gerd Gigerenzer's article challenges the traditional view of cognitive biases in probabilistic reasoning, arguing that many so-called "errors" are not violations of probability theory but rather result from conceptual misunderstandings. He critiques the "heuristics and biases" framework, which has dominated social psychology and related fields, for its narrow normative standards and failure to recognize fundamental distinctions in probability theory, such as single-case versus frequency reasoning. Gigerenzer suggests that by applying these distinctions, apparent "errors" can be eliminated, reappear, or even invert. He argues that intuitive reasoning is highly sensitive to these distinctions, leading to a revised understanding of judgment under uncertainty. The article discusses several cognitive illusions, including overconfidence bias, the conjunction fallacy, and the base-rate fallacy. Gigerenzer shows that these are not necessarily violations of probability theory but rather result from the way people interpret single-event probabilities versus long-run frequencies. He provides examples where rephrasing problems in terms of frequencies rather than single events leads to more accurate judgments, demonstrating that the "errors" identified by the "heuristics and biases" program are not always flawed reasoning but can be explained by a more accurate understanding of probability. Gigerenzer also highlights the importance of random sampling in probabilistic reasoning, arguing that many studies fail to account for this crucial distinction. He shows that when problems are rephrased in terms of frequencies, subjects' judgments align more closely with Bayesian predictions, indicating that the "errors" identified in the "heuristics and biases" framework may be due to a misunderstanding of probability theory. Overall, Gigerenzer's work challenges the traditional view of cognitive biases and offers a more nuanced understanding of judgment under uncertainty.