Statistical Thinking: No One Left Behind

Abstract 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, research conducted in the heuristics-and-biases program during the 1970s and 1980s concluded the opposite. To overcome this striking contradic-tion, psychologists more recently began to identify and characterize the circum-stances under which people—both children and adults—are capable of sound prob-abilistic thinking. One important insight from this line of research is the power of representation formats. For instance, information presented by means of natural fre-quencies, numerical or pictorial, fosters the understanding of statistical information and improves probabilistic reasoning, whereas conditional probabilities tend to im-pede 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 chil-dren or adults, laypeople or experts—to reason better with statistical information. For example, using natural frequencies to convey statistical information helps peo-ple to perform better in Bayesian reasoning tasks, such as understanding the impli-cations of diagnostic test results, or assessing the potential benefits and harms of medical treatments. Teaching statistical thinking should be an integral part of com-prehensive education, to provide children and adults with the risk literacy needed to make better decisions in a changing and uncertain world