# MOOC-ED: Teaching Statistics Through Inferential Reasoning

with  Hollylynne Lee and Gemma Mojica
Complexity Tutorials
Santa Fe University via Complexity Explorer

Every day we have opportunities to make data-based decisions and to consider how information we gather can inform us about what can be claimed about a situation, process, or larger collection. To prepare the next generation of data active citizens, we need to engage learners of all ages in investigations focused on making inferences and claims, supported by samples of data. This course allows you to learn, along with colleagues from other schools, how to emphasize inferential reasoning in teaching statistics through posing different types of investigative questions.

## Syllabus

Unit 0: Orientation and Review of SASI Framework
In Orientation, you are introduced to the course and colleagues, and can review essential background material related to a framework for supporting Students’ Approaches to Statistical Investigations (SASI). This material is part of the Teaching Statistics Through Data Investigations course. If you need a review, or if you have not yet had the opportunity, please engage with these materials before you work on Unit 1.

Unit 1: What is Inferential Reasoning?
In this unit, you learn core aspects of inferential reasoning, why it is important in statistics, and how it develops, from informal approaches with early learners to more formal approaches as learners get more sophisticated, as described in the SASI framework. In the Posing Questions phase of an investigation, you will learn three general types of questions that can provide opportunities for students to build inferential reasoning skills. Each type of question will be the focus of the next three units.

Unit 2: Inferential Reasoning with Comparing Groups
In this unit, we take a deep dive into questions that provide opportunities for learners to compare two or more groups. When learners have a need to find similarities or difference among distributions, their understanding of key characteristics of distributions becomes an essential aspect of making comparative statements and generalizing beyond the data at hand. You will get to experience investigating a comparing groups question, see samples of students work, and consider other tasks for their potential to promote inferential reasoning.

Unit 3: Inferential Reasoning Between Samples and Population
Generalizing from a sample to a population is often considered the quintessential way to make inferences in statistics. In Unit 3, we consider questions that engage learners in considering what is likely true about a population. You will get to experience a task that includes reasoning about a sample to make claims about a population, view students’ work, and consider different ways to support inferential reasoning.

Unit 4: Inferential Reasoning with Competing Models
Unit 4 focuses on how learners can engage with questions that focus on making decisions about which model is the most plausible for describing a population. Within tasks that engage learners to compare competing models, you will consider how learners can use different approaches and levels of sophistication for supporting claims.

Unit 5: Making Inferential Reasoning Essential in Your Practice
This unit will assist you in making plans to change teaching practices that can really engage students in inferential reasoning. You will reflect on, assess, and share what you have learned throughout the course.

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Cost Free Online Course
Pace Finished
Provider MOOC-ED
Language English
Certificates Certificate Available
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