This is a session of Topic 6: Innovation and reform in teaching probability within statistics

Bayesian inference (probability) goes to school: meanings, tasks and instructional challenges

Organizer

• Per Nilsson (Sweden)

Abstract

Objective probability dominates the teaching of probability in our schools. Students are encouraged to learn the importance of taking into account the sample space of a random event and develop techniques to discern the underlying sample space. There are also an increased focus of getting students to understand the principles of empirical probability and of how a random process behaves in the short run and the long run. In our everyday lives and work however, it is seldom we can apply an objective notion of probability based on the assumption of equiprobability or on the assumption that a random process is possible to repeat infinitely many times. In our everyday lives and work, we usually need to determine probabilities when it is not possible to base the models on sample space or frequency information. We need to combine and draw conclusions from multiple, often dependent, sources of information. We need to be able to identify and derive the causes of an event, or find out from which of several alternative processes or random generators a given outcome arise from. This implies the need to develop thinking in line with subjective probability, which means that we need to develop students’ ability to reason in accordance to Bayesian probability theory. Hence, the present session invites papers that is innovative in the sense that it brings to the fore research that highlights a content that has received limited attention in both school and in research up to date.

Papers