Department of Biostatistics Seminar/Workshop Series

Ruminations on Statistical Evidence

Jeffrey Blume, PhD

Associate Professor, Department of Biostatistics
Vanderbilt University School of Medicine

Wednesday, December 10, 1:45-2:55pm, MRBIII Conference Room 1220

Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials methodology, statistical computing, statistical graphics, R users or potential users

Science looks to Statistics for an objective measure of the strength of evidence in a given body of observations. But Statistics offers only a patchwork of borrowed principles and tools from decision and belief theory to guide the assessment of evidence in data. A ‘theory of evidence’ has yet to be established and, as a result, statistical assessments of evidence are beset with irresolvable controversies, such as those surrounding the proper use and interpretation of p-values or those concerning adjustments for multiple comparisons and multiple looks at data.

In this talk, I’ll examine this patchwork and argue that it does not substitute for a general ‘theory of evidence’, which is sorely needed. We’ll see that a ‘theory of evidence’ must distinguish between three key evidential quantities and that this mere distinction resolves the aforementioned controversies. These concepts are (1) a measure of the strength of evidence, (2) the probability that a particular study design will generate misleading or weak evidence, and (3) the probability that an already observed measure of evidence is misleading. Because such a distinction does not exist in today’s Frequentist or Bayesian methodologies, I’ll appeal to the Likelihood paradigm to illustrate these concepts in some simple examples and two well known clinical trials.
Topic revision: r2 - 26 Apr 2013, JohnBock
 

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