Applied Adaptive Statistical Methods: Tests of Significance and Confidence Intervals (ASA-SIAM Series on Statistics and Applied Probability, Series Number 12)

Adaptive statistical tests, developed over the last 30 years, are often more powerful than traditional tests of significance, but have not been widely used. To date, discussions of adaptive statistical methods have been scattered across the literature and generally do not include the computer programs necessary to make these adaptive methods a practical alternative to traditional statistical methods. Until recently, there has also not been a general approach to tests of significance and confidence intervals that could easily be applied in practice. Modern adaptive methods are more general than earlier methods and sufficient software has been developed to make adaptive tests easy to use for many real-world problems. Applied Adaptive Statistical Methods: Tests of Significance and Confidence Intervals introduces many of the practical adaptive statistical methods developed over the last 10 years and provides a comprehensive approach to tests of significance and confidence intervals.

Thomas W. O’Gorman is Associate Professor of Statistics at Northern Illinois University. He has worked as a statistical consultant at the University of Iowa and has conducted statistical research for Southwestern Bell Telephone Company. He has written numerous articles and is a member of the American Statistical Association, the Statistical Society of Canada, and the Biometric Society.