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  \textbf{\textsc{STANFORD UNIVERSITY}}\\[5pt]
  \textbf{\textsc{DEPARTMENT OF STATISTICS}}\\[5pt]
  \Large{\textbf\textsc{{DEPARTMENTAL SEMINAR}}}
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  4:15 p.m., Wednesday, July 5, 2006\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{ Bradley Efron   }\\
Department of Statistics, Stanford University
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  \textbf{  Doing Thousands of Hypothesis Tests at the Same Time  } 
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\noindent
Classic multiple comparisons theory, beginning with the Bonferroni bound, was aimed
at preserving frequentist p-values when simultaneously dealing with several testing
problems. ``Several'' meant two to perhaps twenty. Modern scientific technology - microarrays being the prototype - routinely confront the statistician with thousands of
simultaneous tests. Statistical inference can be qualitatively different in such
situations, with Bayes or empirical Bayes ideas forcing themselves even upon
dedicated frequentists. The emphasis will be more on examples and general guide-lines than on theory.

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