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BANISHING THE THEORY-APPLICATIONS DICHOTOMY FROM STATISTICS EDUCATION

BANISHING THE THEORY-APPLICATIONS DICHOTOMY FROM STATISTICS EDUCATION. Larry Weldon Department of Statistics and Actuarial Science Simon Fraser University, Burnaby, CANADA. ?. ?. “Issue” Questions. Is Mathematical Statistics = Theory of Statistics?

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BANISHING THE THEORY-APPLICATIONS DICHOTOMY FROM STATISTICS EDUCATION

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

  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? weldon@sfu.ca

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