Statistical literacy, that is, the ability to make sense of data, is becoming increasingly important for one to fully engage as a citizen. Daily we are confronted with questions that can only be answered by taking accurate measurements and then analyzing those measurements, possibly comparing them to other measurements. Young children do this repeatedly, from comparing their portion of ice cream to their sister’s, or noting how many pets one family has compared to another, to observing variation in autumn leaves, or estimating the time it takes to do various activities (“We have time to watch a movie before bedtime, don’t we?”). Children are naturals at analyzing the world around them. Given this inherent interest and the importance later in life for responsible citizenship, it’s no wonder that the Common Core State Standards emphasize developing skills in measurement and data analysis as early as kindergarten.
Prior to the development of the Common Core standards, the National Council of Teachers in Mathematics called for increased emphasis on data analysis that would span the grades. In the early grades students need opportunities to develop their understanding that individuals and objects have attributes. Measuring attributes, especially when some estimation is involved, can have the added benefit of enhancing number sense. Analyzing (often involving graphing) the resulting data can answer questions for which answers are not obvious. As students advance, the ability to describe data can extend to drawing inferences about their world. That connection between math and their world can be essential for not only developing mathematical skills, but motivating students to want to learn more mathematics as they grow.
Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report: a Pre-K-12 Curriculum Framework notes that statistical problem solving is an investigative process that involves the four components:
I. Formulate Questions
→ clarify the problem at hand
→ formulate one (or more) questions that can be
answered with data
II. Collect Data
→ design a plan to collect appropriate data
→ employ the plan to collect the data
III. Analyze Data
→ select appropriate graphical and numerical
→ use these methods to analyze the data
IV. Interpret Results
→ interpret the analysis
→ relate the interpretation to the original question
This framework can be followed for projects appropriate for kindergartners up through undergraduates. The links below are intended to provide the necessary background, resources, and ideas for applying this framework to the classroom.