Statistical Thinking: No One Left Behind
Is the mind an “intuitive statistician”? Or are humans biased and error-prone when it comes to probabilistic thinking? While researchers in the 1950s and 1960s suggested that people reason approximately in accordance with the laws of probability theory, r
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Abstract Is the mind an “intuitive statistician”? Or are humans biased and errorprone when it comes to probabilistic thinking? While researchers in the 1950s and 1960s suggested that people reason approximately in accordance with the laws of probability theory, research conducted in the heuristics-and-biases program during the 1970s and 1980s concluded the opposite. To overcome this striking contradiction, psychologists more recently began to identify and characterize the circumstances under which people—both children and adults—are capable of sound probabilistic thinking. One important insight from this line of research is the power of representation formats. For instance, information presented by means of natural frequencies, numerical or pictorial, fosters the understanding of statistical information and improves probabilistic reasoning, whereas conditional probabilities tend to impede understanding. We review this research and show how its findings have been used to design effective tools and teaching methods for helping people—be it children or adults, laypeople or experts—to reason better with statistical information. For example, using natural frequencies to convey statistical information helps people to perform better in Bayesian reasoning tasks, such as understanding the implications of diagnostic test results, or assessing the potential benefits and harms of medical treatments. Teaching statistical thinking should be an integral part of comprehensive education, to provide children and adults with the risk literacy needed to make better decisions in a changing and uncertain world.
B. Meder (B) · G. Gigerenzer Center for Adaptive Behavior and Cognition (ABC), Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany e-mail: [email protected] G. Gigerenzer Harding Center for Risk Literacy, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany E.J. Chernoff, B. Sriraman (eds.), Probabilistic Thinking, Advances in Mathematics Education, DOI 10.1007/978-94-007-7155-0_8, © Springer Science+Business Media Dordrecht 2014
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B. Meder and G. Gigerenzer
1 Introduction Is the mind an “intuitive statistician”? Or are humans biased and error-prone when it comes to probabilistic thinking? Several studies have addressed these questions, giving very different answers. Some research suggests that, by and large, people’s intuitive inferences approximately correspond to the laws of probability calculus, mirroring the Enlightenment view that the laws of probability are also the laws of the mind (Daston 1988). Other studies indicate that people have severe difficulties with probability judgments, which has led researchers to conclude that the human mind is not build to reason with probabilities (Tversky and Kahneman 1974). Much of the work used to corroborate either of the two positions has focused on simple statistical inference problems. Consider a woman applying a home pregnancy test kit. If she tests positive, the probability that she is pregnant increases
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