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BANISHING THE THEORY-APPLICATIONS DICHOTOMY FROM STATISTICS EDUCATION Larry Weldon Department of Statistics and Actuarial Science Simon Fraser University,

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Presentation on theme: "BANISHING THE THEORY-APPLICATIONS DICHOTOMY FROM STATISTICS EDUCATION Larry Weldon Department of Statistics and Actuarial Science Simon Fraser University,"— Presentation transcript:

1 BANISHING THE THEORY-APPLICATIONS DICHOTOMY FROM STATISTICS EDUCATION Larry Weldon Department of Statistics and Actuarial Science Simon Fraser University, Burnaby, CANADA

2 “Issue” Questions Is Mathematical Statistics = Theory of Statistics? Expert vs Practitioner vs Generalist different stats education? Motivation for practitioner grps? What undergrad course sequences? – for practitioners – for experts Motivation for Stats Instructors? ? ? Implications for Stats Course Taxonomy

3 Some Questions Is Mathematical Statistics = Theory of Statistics? Expert vs Practitioner vs Generalist different stats education? Motivation for practitioner grps? What undergrad course sequences? – for practitioners – for experts Motivation for Stats Instructors? ? ?

4 Basic Theory: More than Math? Obs Study vs Experiment Distributions: Averages and Variability Random Sampling, Estimation Independence (and dependence) Time Series Statistical Significance

5 Example: Dependence When does a portfolio of stocks have enough independence to provide stability of return? One needs to understand the dependence-independence concept A & B independent -> P(A&B)=P(A)*P(B) is not enough

6 Basic Theory: More than Math? Obs Study vs Experiment Distributions: Averages and Variability Random Sampling, Estimation Independence (and dependence) Time Series Statistical Significance Theory = Generally Applicable Concepts (Much more than Mathematics)

7 Question Answered? Is Mathematical Statistics = Theory of Statistics? No! Theory is Generally Applicable Concepts. ? ? More Questions ->

8 Some Questions Is Mathematical Statistics = Theory of Statistics? Expert vs Practitioner vs Generalist different stats education? Motivation for practitioner grps? What undergrad course sequences? – for practitioners – for experts Motivation for Stats Instructors? ? ?

9 Levels of Expertise Generalist – requires stats appreciation Practitioner – requires stats appreciation – requires stats methods & hazards – requires exposure to expert capability Expert – all the above and much more Cumulation Model of Statistics Education

10 Do Practitioners need “Appreciation” Course? Overview for when-to-consult Motivation to integrate with applied focus Awareness of naïve user (hazards)

11 Experts need “stats appreciation”? Yes, because they need informed choice of career Real expert statisticians are generalists as well as specialists, so they can absorb context Need to explain to naïve user

12 Experts need “Practitioner” training? of course! early exposure helps education no need to learn everything the hard way Proposed Course Sequence: Appreciation -> Practitioner -> Expert Questions ->

13 Some Questions Is Mathematical Statistics = Theory of Statistics? Expert vs Practitioner vs Generalist different stats education? Motivation for practitioner grps? What undergrad course sequences? – for practitioners – for experts Motivation for Stats Instructors? ? ?

14 Motivation Clusters? Does “auto engine size” or “golf participation” interest biologists? Does “potato pest resistance” or “threatened species of birds” interest social scientists? Contextual Interest is Important for Seeking Data-Based Information

15 Stats Streams for Major Groups? General (Wide Focus) Life Science Social Science Important for early courses, perhaps not feasible for higher level ones. Context Material Matters! Because Context-Major Students chose context! Minimal Context Segregation for Courses … (segregation by context … not by methods introduced) Questions ->

16 Some Questions Is Mathematical Statistics = Theory of Statistics? Expert vs Practitioner vs Generalist different stats education? Motivation for practitioner grps? What undergrad course sequences? – for practitioners – for experts Motivation for Stats Instructors? ? ?

17 Undergrad Course Structure? Statistics 1 (life) Statistics 1 (social) Statistics 1 (general) (Appreciation courses) Statistics 2 (life) Statistics 2 (social) Statistics 2 (general) Statistics 3 (life) Statistics 3 (social) Statistics 3 (general) (Practitioner Courses) Statistics 4 (general) Statistics 5 (general) Statistics 6 (general) (Expert courses) More courses where numbers permit. Note: 1. No specialized technique courses like Nonparametrics, Time Series, Experimental Design, Quality Control, Bayesian Analysis 2. No “service” stream 3. No “baby” stat courses Experts need “MORE” not “DIFFERENT”

18 Experiential Learning&Teaching Sequence of Projects – data collection – data analysis – data summary Techniques as Required Concepts as they Arise Example ->

19 Experiential Learning Examples Sports Leagues – probability – measures of variability – simulation Daily Delivery Schedules – censored data (demand exceeds sales) – parametric variability, prediction – optimization Many concepts and techniques will be introduced Questions ->

20 Some Questions Is Mathematical Statistics = Theory of Statistics? Expert vs Practitioner vs Generalist different stats education? Motivation for practitioner grps? What undergrad course sequences? – for practitioners – for experts Motivation for Stats Instructors?

21 Case Studies/Projects – experiential learning Discussion & Presentations Novelty and Creativity encouraged Active engagement of students and instructors Better Use of Instructor Expertise & Experience

22 Motivation for Stats Instructors? Case Studies/Projects – experiential learning Discussion & Presentations Novelty and Creativity encouraged Active engagement of students and instructors Better Use of Instructor Expertise & Experience

23 Motivation for Stats Instructors? Case Studies/Projects – experiential learning Discussion & Presentations Novelty and Creativity encouraged Active engagement of students and instructors Better Use of Instructor Expertise & Experience

24 Motivation for Stats Instructors? Case Studies/Projects – experiential learning Discussion & Presentations Novelty and Creativity encouraged Active engagement of students and instructors Better Use of Instructor Expertise & Experience

25 Motivation for Stats Instructors? Case Studies/Projects – experiential learning Discussion & Presentations Novelty and Creativity encouraged Active engagement of students and instructors Better Use of Instructor Expertise & Experience

26 Summary Experiential Learning is Authentic Learning It can be motivating for most students and instructors It can be efficient in reducing the number of courses offered Levels of expertise correspond to number of courses completed (not math level) Downside? Requires instructors with an interest in, and experience with, using statistical theory. Thanks for attending this session. Comments?


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