The main objective in this paper is to describe a framework to characterize and assess the learning of<br>elementary statistical inference. The key constructs of the framework are: populations and samples and their<br>relationships; inferential process; sample sizes; sampling types and biases.<br>To refine and validate this scheme we have taken data from a sample of 49 secondary students sample<br>using a questionnaire with 12 items in three different contexts: concrete, narrative and numeric. Theoretical<br>analysis on the results obtained in this first research phase has permitted us to establish the key constructs<br>described below and determine levels in them. Moreover this has allowed us to determine the students'<br>conceptions about the inference process and their perceptions about sampling possible biases and their<br>sources.<br>The framework is a theoretical contribution to the knowledge of the inferential statistical thinking domain<br>and for planning teaching in the area.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education