This chapter focuses on the development of statistics cognition, framing the discussion on the need to enhance cognitive development. We argue that by understanding how cognitive processes have been shown to inhibit learning, we can differentiate between types of errors in statistics education. By understanding the operationalized difference of a bias and misconception, those interested in statistics education can identify the sources of these errors, and subsequently develop a means to attenuate their effect. Using dual process theory, we argue that classifying the source of errors and differentiating between biases and misconceptions educators can use errors to enhance the development of statistics literacy, reasoning, and thinking.
Although none of us can know the future, predicting it is so important that organisms are hard wired to learn about predictable events in their environment. - Andy Field, 2013, p. 197
Top2. Cognitions Used To Learn
The ultimate goal of statistics education has become to enhance statistics cognitions. As discussed, quantifying cognitions is difficult (Chapter 2), but is of central interest to educators and researchers who have a vested interested in statistics education. Although ways that cognition could be quantified have been outlined, an important assumption made by a cognitively derived curriculum is that cognitions can be enhanced. Namely, students in a statistics class should retain working knowledge of important statistical concepts, be able to demonstrate sound rationale for their statistical practices, and be able to apply a set of skills relevant toward statistics by the end of the course (Kundert, 1990; Mallows, 1998; Woolley, 2013). However, students come into classrooms with their own cognitions, often related to factors that an educator has no reasonable control over (Wright et al., 2001). Thus, it is not enough to establish the types of cognitions we would like to see from students, but understand how cognitions themselves are part of the learning process.