Webinars

Students need exposure to Bayesian thinking at early stages in their mathematics and statistics education. While many students in upper level probability courses can generally recite the differences in the Frequentist and Bayesian inferential paradigms, these students often struggle using Bayesian methods when conducting data analysis. Specifically, students tend to struggle translating subjective belief to the specification of a prior distribution and the incorporation of uncertainty in the Bayesian inferential approach. The purpose of this webinar is to present a hands-on activity involving the Beta-Binomial model to facilitate an intuitive understanding of the Bayesian approach through subjective problem formulation which lies at the heart of Bayesian statistics.

Eiki's presentation begins at the 28 minute mark. See Part 1.

We present a data set and case study exercise that can be used by educators to teach a range of statistical concepts including Simpson’s paradox. The data set and case study are based on a real-life scenario where there was a claim of discrimination based on ethnicity. The exercise highlights the importance of performing rigorous statistical analysis and how data interpretations can accurately inform or misguide decision makers.

Wikipedia's page on Statistics Education gets hundreds of hits every week, but until recently the page gave a very limited impression of our discipline. A group at the University of Minnesota has been regularly meeting since fall 2012 to research, update, and improve the Wikipedia coverage of statistics education. We have only begun to scratch the surface of Wikipedia's power to collect and widely disseminate the what, when, who, where, and why of teaching and learning statistics. Come hear about what we've done so far, and how you can get involved in spreading the word about the resources available to statistics educators worldwide.

Histograms are adept at revealing the distribution of data values, especially the shape of the distribution and any outlier values. They are included in introductory statistics texts, research methods texts, and in the popular press, yet students often have difficulty interpreting the information conveyed by a histogram. This talk will identify and discusses four misconceptions prevalent in student understanding of histograms. In addition, pre- and post-test results on an instrument designed to measure the extent to which the misconceptions persist after instruction will be presented. The results indicate not only that some of the misconceptions are commonly held by students prior to instruction, but also that they persist after instruction. Future directions for teaching and research are considered.

Active learning opportunities can be difficult to generate when teaching large groups of students. In this webinar, I will present an experiment using Sudoku puzzles that can be easily conducted in a lecture with 300 (or more) students. The factor manipulated in the experiment is the type of Sudoku puzzle and there are four types, which are each the same puzzle but with different characters. The experiment yields a rich data set which can be used to illustrate basic statistical methods such as chi-square test for independence of categorical variables, through to more complicated analyses such as survival analysis techniques. I will outline the experiment and give an overview of the teaching opportunities that the data present.

The Common Core State Standards (CCSS) include much more statistics content than previous standards. Their adoption has created the opportunity and necessity for nearly all middle school and high school mathematics teachers to be prepared to teach a substantial amount of statistics. This session will focus on the topic of sampling variability, a topic that is greatly emphasized in the middle and high school grades in the CCSS. We will present a research-based learning trajectory to help guide teacher preparation on this topic. In addition, we will discuss several unexpected misconceptions that emerged while testing the trajectory with high school teachers. As a group, we will work through an activity together to illustrate how to use the trajectory with teachers.

As a part of an opening course survey, data on eye color and gender were collected from students enrolled in an introductory statistics course at a large university over a recent four year period. Biologically, eye color and gender are independent traits. However, in the data collected from our students, there is a statistically significant dependence between the two variables. In this article, we present two ideas for using this data set in the classroom, and explore the potential reasons for the dependence between the two variables in the population of our students.

Our current students are among the first to have been vaccinated against HPV. Have they ever considered how the accumulation of the evidence for the efficacy of the vaccine resulted in recommendations for its widespread provision? Using data sourced from a meta-analysis of clinical trials for HPV vaccines, we will examine this evidence. We will show how these data can be used to illustrate applications of methods of categorical data analysis, for students in courses at a variety of levels. And we will describe how this case study can be used to promote discussion of concepts in the design of experiments, statistical concerns such as independence of observations, and the importance of context in the interpretation of the results of data analyses.

Like many other research universities, the University of Nebraska-Lincoln relies on graduate student instructors to cover a large portion of the instructional load in the introductory course. In order to better prepare new graduate student instructors, we have implemented a mentoring program that pairs new GTAs with experienced graduate student instructors. Through the mentoring program, the new GTA has a semester to acclimate to graduate school and their new role as instructor, and the senior GTA has the opportunity to emerge as a teacher leader.