Teaching Introductory Statistics in a Digital World

1. Teaching Introductory Statistics in a Digital World The View from the Trenches Friday, January 18, 2018 Joint Mathematics Meetings Baltimore

2. Background

3. 2008

5. MAA Seaway 2008 Statistics Panel

6. AP Statistics Reader, 2013-Present

7. 2015

8. 2015

9. 2016 Tintle made a distinction at this workshop that really resonated with me. He talked about students as being consumers of statistics or producers of statistics.

10. MAA – Stat Sigma 2017 Atlanta

11. After the 2017 MAA After the 2017 MAA I was convinced that experts didn’t have the answer to the introductory statistics class my students need. My students are both consumers and producers of statistics and of data.

12. While SBI gives them great intuition on the means of a p-value, it doesn’t teach them how to analyze data. Students tell me, I know which formula to use but I don’t know how to analyze data.

13. Data and Predictive Analytics Center (2016).

14. What’s New?

15. The first step was revising the intro stats curriculum.

16. How did we get here and why change? • The course we have been teaching for years grew up in a historical context. • That context has changed. • Technology has changed.

17. We have updated our other tools. We don’t do this anymore. So why do we do this? Don’t get me started on TI’s Incentive System!

18. Not All roads Lead to Inferential Statistics. REAL Data is not always a random sample. Often what we have is “the” data so we can’t make the traditional distinction between the sample and the population, between descriptive statistics and inferential statistics.

19. Data Sets aren’t rectangular arrays • Often data is stored in a relational data base for efficiency. When we want to use a rectangular array, we need to wrangle it into that form. • We need to at least show them how a relational data base works.

20. Data Viz is everywhere. • https://www.wou.edu/las/physci/ch412/alttable.htm

21. Data: not just Quantitative and Qualitative anymore. • Google can show where we have been in the last year. • The classes we teach need to reflect that. • Images are data, location is data, signals are data.

22. So, what do I do?

23. Day 1 I did this the first 15 minutes of class.

28. Exploratory Data Analysis is more important than ever before. • We have to catch up. They have been doing univariate descriptive statistics and basic probability since grade school. • Why do we recover that ground instead of teaching Heat Maps, Mosaic Plots, Bubble Plots, and Interactive Graphics .

29. Heatmap

30. Most students can’t see a distinction.

32. Multivariate Analysis is a MUST.

33. We can go beyond simple regression.

36. Go Light on Traditional Probability. • They already know proportions and odds ratios. • That is enough to understand a p-value at a very basic level. • Simulation Based Inference can provide the foundations of Inference rather than getting bogged down in Probability.

37. The Proposal to Lower P Value Thresholds to .005 • JAMA.Published online March 22, 2018. doi:10.1001/jama.2018.1536 • "Moving the P value threshold from .05 to .005 will shift about one-third of the statistically significant results of past biomedical literature to the category of just 'suggestive.'This shift is essential for those who believe (perhaps crudely) in black and white, significant or nonsignificant categorizations. For the vast majority of past observational research, this recategorization would be welcome." • https://jamanetwork.com/journals/jama/fullarticle/2676503?utm_source=twitter&utm_campaign=content-shareicons&utm_content=article_engagement&utm_medium=social&utm_term=032218#.WrQS3lm-Ma0.twitter

38. Decision Trees invite exploration of Simpson’s Paradox.

39. The Confusion Matrix is NOT Confusing. Using Titanic Data The traditional approach is very clever. It uses a lot of logic. But do they understand what it would imply in another context?

40. Decision Trees invite exploration of Simpson’s Paradox.

43. Inferential Statistics is not the only game in town. • Explanatory Modeling v. Predictive Modeling is not above their heads. • To not mention Data Science in this day and age is intellectually lazy or dishonest. not Albert Einstein

44. Reproducibility Crisis.

45. Jan 1, 2019 1:18 AM • Certainly a lot of the reproducibility issue with social science studies arises because studies of people inherently involve greater "between subject" variability than physics or physical chemistry experiments. However, there are many other practices that lead to lack of reproducibility as well. These include poor study design, poor choice of measure, and poor or inappropriate statistical practices in analyzing data.------------------------------Martha SmithUniversity of Texas------------------------------ Obviously we are party animals!

46. Jeffrey Flier, former Dean of Harvard's Medical School at the National Academy of Sciences. He indicated that there is a "statistical deficit" to include a lack of sufficient statistical awareness; and what some view as inadequate training in statistics. The inadequacy is especially evident in clinical research (Dialog and Reactions, Arthur M. Sackler Colloquia 2017).------------------------------Esther PearsonAssistant ProfessorLasell College

47. "Insanity is doing the same thing over and over again and expecting different results." • The "replication crisis" is certainly getting a lot of attention in a variety of fields these days.  • Michael GranaasUniv. of South Dakota not Albert Einstein

48. R

49. R There has GOT to be an APP for that!

50. R