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Robust

Robust. Problem. Even small deviations from normality render the traditional statistically techniques inaccurate . Even small deviations from normality destroys power (our ability to find true relationships or association between variables).

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Robust

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  1. Robust

  2. Problem • Even small deviations from normality render the traditional statistically techniques inaccurate. • Even small deviations from normality destroys power (our ability to find true relationships or association between variables). • “All of the hypothesis testing methods taught in a typical introductory statistics course are obsolete, there are no exceptions. Hundreds of articles have pointed this out, and no published article to date has given a counterargument as to why we should continue with standard techniques.”

  3. More details… • Three features of conventional methods… • Skewness • Outliers • Heteroscedasticity • … results in: • Poor power • Inaccurate confidence intervals • Poor control over type 1 error • Inaccurate effect sizes

  4. Solution • New techniques have been developed that make the data “normal” and/or don’t rely upon normality • FYI – “Robust” is different than “non-parametric” in that nonparametric avoid assumptions whereas robust account for non-normality

  5. Why doesn’t everyone use “Robust” http://www.psychologicalscience.org/observer/getArticle.cfm?id=931 • Difficult to change the status quo because… • “Commercial software” • “Introductory statistics textbooks” • “Anyone can teach stats” • “Students won’t understand” • “Disciplinary attitudes” • Apprenticeship model of learning

  6. The world of “Robust” statistics • Detect skewness • Detect outliers • Detect Heteroscedasticity (detect breakdown point, rejection point, sensitivity, etc) • Transformations (estimators, smooths, etc) • Trimming (and/or with replacement) • Bootrapping (and/or with replacement) • Formulas that account for problems • Know when to use each one based upon your data

  7. Software • R is free. Download it at http://cran.R-project.org/ Also see www.R-project.org. • S+ is expensive Go to www.insightful.com

  8. More information • Part 1 – Basic principles http://www-rcf.usc.edu/~rwilcox/documents/work1_001.pdf • Part 2 – Comparing groups http://www-rcf.usc.edu/~rwilcox/documents/work2_000.pdf • Part 3 – Regression http://www-rcf.usc.edu/~rwilcox/documents/work3_000.pdf • Part 4 – Using R and S+ http://www-rcf.usc.edu/~rwilcox/documents/work4_000.pdf

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