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Statistician drowning in river of average depth 25 cm

Statistician drowning in river of average depth 25 cm. A.N. Whitehead. “The things directly observed are, almost always, only samples. We want to conclude that the abstract conditions… also hold for all the other entities which… appear to us to be of the same sort.”.

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Statistician drowning in river of average depth 25 cm

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  1. KNAW Lecture

  2. Statistician drowning in river of average depth 25 cm KNAW Lecture

  3. A.N. Whitehead • “The things directly observed are, almost always, only samples. We want to conclude that the abstract conditions… also hold for all the other entities which… appear to us to be of the same sort.” KNAW Lecture

  4. Observation in the face of variation • Video ergo lego • (I see therefore I select) KNAW Lecture

  5. Effect of selection on observation KNAW Lecture

  6. KNAW Lecture

  7. Vulnerability Analysis of Spitfires (sample: 15/400) KNAW Lecture

  8. * * * * * * * * * * * * * * Composite of hits Abraham Wald Advice: KNAW Lecture

  9. Numbers speak for themselves. Not. KNAW Lecture

  10. Another anatomy of missingness • …as we know, • there are known knowns; • there are things we know we know. • We also know there are known unknowns; • that is to say, • we know there are some things • we do not know. • But there are also unknown unknowns— • the ones we don’t know we don’t know. • Donald Rumsfeld (set to music, see NPR website) KNAW Lecture

  11. Translation into modern statistics Non-missing MCAR/MAR Non-ignorable • …as we know, • there are known knowns; • there are things we know we know. • We also know there are known unknowns; • that is to say, • we know there are some things • we do not know. • But there are also unknown unknowns— • the ones we don’t know we don’t know. • Donald Rumsfeld KNAW Lecture

  12. Summary of Part A • Variation in everyday life • Observation is selection • Random selection gold standard • Lack of randomness is challengeto valid inference KNAW Lecture

  13. Causal assertion. Is it testable? KNAW Lecture

  14. Question Testable version KNAW Lecture

  15. Example from Science (February 23, 2007) • Title Redefining the age of Clovis • Front page Flints (pictures) • Page 1045 Summary paragraph • Page 1067 News story • Page 1122—1126 Article (numbers) • Very different “flavor” for each section KNAW Lecture

  16. Observational vs experimental studies • Characteristic Observational Experiment • Orientation Retrospective Prospective • Researcher control Less More • Selection bias Big problem Less • Confounding Present Absent • Realism More Less • Causal plausibility Weaker Stronger • Analysis More complicated Less • Ethical issues Fewer More • Inference Weaker Stronger KNAW Lecture

  17. ? Effect Usual state of nature KNAW Lecture

  18. APHA Journal, May, 2004 KNAW Lecture

  19. Conclusion: we always need to ask: • What is the question? • Is it measurable or testable? • Where will I get the data? • What do I think the data are telling me? KNAW Lecture

  20. Variation Causation Experience can benefit from everyday statistics • Variation is fact of life • “Population” as model • Regression to the mean • Random selection • What is the question? • Testable question? • Association or causation • Causation through randomization KNAW Lecture

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