1. CAUSE Webinar Tuesday, Dec. 11 th, 2007 "Content Barriers to Effective Pedagogy in the Introductory Statistics Course" Mark L. Berenson Montclair State University

2. Webinar Abstract As we consider how we might improve our introductory statistics course we are constrained by a variety of environmental/logistical and pedagogical issues that must be addressed if we want our students to complete the course saying it was useful, relevant and practical. If not properly addressed, such issues may result in the course being considered unsatisfying and/or unnecessarily obtuse.

3. Webinar Goals This Webinar will focus on key course content concerns that must be addressed and will engage participants in discussing resolutions. Participants will also have the opportunity to describe and discuss other content barriers to effective statistical pedagogy.

4. Motivation Before the semester, as we prepare to teach the introductory course again, we can “rethink”: What did we do last time? How did we do it? How can we do it better this time?

5. Issues to Contemplate Class size Class homogeneity –Students’ mathematical preparation –Students’ technological preparation Classroom capability –Computers –Doc Camera –Overhead projector –Clickers –Shape of room and seating

6. Other Considerations Course topics to be selected -Multi-section course with uniform syllabus? -Service course for other departments? Textbook to be selected -Classroom notation concordant with text notation? Computer software selected -Software is a tool?

7. Having a Goal and Keeping Your Eye on the Prize Regardless of how we decide to address the aforementioned issues, we will still be faced with various pedagogical dilemmas concerning course content (i.e., “Peck’s Gorillas”) that need to be thoroughly considered if we want our students to leave the course saying: It was useful, relevant and practical It increased critical thinking and analytic skills It increased communication skills

8. Content Barriers (“Peck’s Gorillas”) 1.Addressing Statistics Language Barrier 2.Choosing Software 3.Observing Bad vs Good Graphics 4.Presenting Tables of Z, t, F and 5.Teaching a Z-Test with Numerical Data

9. Addressing Statistics Language Barrier Bootstrapping Collectively Exhaustive Hinges Multicollinearity Point Biserial Correlation Robust Standard Normal Deviate

10. Bootstrapping

11. Collectively Exhaustive

12. Hinges

13. Multicollinearity

14. Point-biserial correlation

15. Robust

16. Standard Normal Deviates

17. Choosing Software Professional Package (JMP, Minitab or SPSS) Textbook Add-ons (PHStat) Microsoft Excel Hand-held Calculator “God-given” Calculator

18. Observing Bad & Good Graphics Multiple Choice: Why does there seem to be a plethora of bad graphics shown in newspapers, magazines and reports? A.There are no daVincis left. Artists are typically mathematically illiterate. B.Artists and statisticians don’t seem to communicate. C.A graph must attract attention to sell.

19. Bad Graph Resource: The NEW YORK TIMES, Firday, April 20, 2007

20. Bad Graph

22. To enhance the learning process, students need to make connections quickly. The current textbooks fail to facilitate the learning process when presenting and demonstrating the use of statistical tables. Presenting Tables of Z, t, F and

23. Z Table

24. Z Table--Better

25. T Table

26. T Table--Better

27. Table df

28. Table--Better

29. F Table

30. F Table--Better

31. Teaching a Z-Test with Numerical Data The Three Questions: 1-Why do we do it? 2-How do we do it? 3-How can we cover the topic better? “Making the Teaching of Statistical Inference More Effective in Business Schools” Mark L. Berenson and Kimberly Killmer Hollister (Presented at DSI, November 2007)

32. Change the Way Inference is Covered in the Texts Introductory statistics books must be made more practical. –Responsibility: Publishers/editors Authors Instructors –Encourage authors to remove all end-of-section and all end-of-chapter exercises using "sigma known." Such exercises cause problems by making the subject of statistical inference unrealistic. Such exercises cause problems by making the subject of statistical inference confusing. (How can we know “ sigma ” but not “ mu ” ? )

33. The Root Causes of the Problem Background of faculty teaching statistics Faculty with sufficient formal and practical training. Faculty with insufficient formal and/or practical training. Graduate students. Texts and software with inappropriate materials Texts provide significance tests and confidence interval estimates with “ sigma known. ” Widely used software such as Microsoft Excel 2007 and Minitab Version 14 provide significance tests and confidence interval estimates with “ sigma known. ”

34. Content Barrier to Teaching Inference Traditional textbook topic sequencing and follow-up classroom instruction lead to a confounding of “ concepts and theory ” with “ practice ” by weaker students, resulting in the mixing of applications requiring Z and t.

35. Moves to Change the Teaching of Introductory Statistical Inference Conover and Iman – for over 20 years have said teach it through a nonparametric/distribution- free approach instead of traditional normal theory approach. MSMESB – for the past 20 years have said “ limit it if not eliminate it. ” USCOTS/ASA Statistical Education – in recent years have said teach it through resampling techniques instead of traditional normal theory approach. Berenson and Killmer-Hollister – have been saying teach the traditional normal theory approach to business students from a more practical and realistic perspective by altering topic sequence and demonstrating actual inferential applications.