Chapter 20: The Multidimensional Learning Goals For Making Inferences With Data

In the Age of Inference, making inferences with data requires a diverse set of ideas and practices. Data, data visualizations, and models make use of mathematical and statistical ideas related to quantity, variability, space, and chance. Nevertheless, these ideas are never independent of context. Data are generated to inform a question about something (Cobb & Moore, 1997; Wild et al., 2018). This means that knowledge of the investigation context must be knit with the mathematical and statistical knowledge in order to make inferences with data, which is always an interdisciplinary act.

Schools, though, are often organized around disciplinary boundaries, such as mathematics and science. Students go to one class to learn math, another to learn science, and another to learn about communicating with an audience. As they move across these boundaries, it is common for students to see the various classes as disconnected from each other. This creates significant challenges for supporting the development of the ideas and practices necessary for making inferences with data because students must develop an integrated, coherent set of interdisciplinary ideas and practices across multiple years of schooling (Lehrer & English, 2018). How will students be able to do this without support to see connections across disciplinary boundaries?