In the past two decades, striking examples of allegedly inferior likelihood ratio tests (LRT) have appeared in the statistical literature. These examples, which arise in multiparameter hypothesis testing problems, have several common features. In each case the null hypothesis is composite, the size LRT is not similar and hence biased, and competing size tests can be constructed that are less biased, or even unbiased, and that dominate the LRT in the sense of being everywhere more powerful. It is therefore asserted that in these examples and, by implication, many other testing problems, the LR criterion produces ‘‘inferior,’’ ‘‘deficient,’’ ‘‘ undesirable,’’ or ‘‘flawed’’ statistical procedures.

This message, which appears to be proliferating, is wrong. In each example it is the allegedly superior test that is flawed, not the LRT. At worst, the ‘‘superior’’ tests provide unwarranted and inappropriate inferences and have been deemed scientifically unacceptable by applied statisticians. This reinforces the well-documented but oft-neglected fact that the Neyman-Pearson theory desideratum of a more or most powerful size test may be scientifically inappropriate; the same is true for the criteria of unbiasedness and -admissibility. Although the LR criterion is not infallible, we believe that it remains a generally reasonable first option for non-Bayesian parametric hypothesis-testing problems.

Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA)provides a coherent mechanism for accounting for this model uncertainty. Several methods for implementing BMA have recently emerged. We discuss these methods and present a number of examples.In these examples, BMA provides improved out-of-sample predictive performance. We also provide a catalogue of currently available BMA software.

.The paper discusses the contributions Student W. S. Gosset made to the three stages in which small-sample methodology was established in the period 1908–1933: (i) the distributions of the test-statistics under the assumption of normality, (ii) the robustness of these distributions against nonnormality, (iii) the optimal choice of test statistics. The conclusions are based on a careful reading of the correspondence of Gosset with Fisher and E. S. Pearson.

The literature describing methods for estimating animal abundance and related parameters continues to grow. This paper reviews recent developments in the subject over the past seven years and updates two previous reviews.

Chin Long Chiang, Professor in the Graduate School, Univer- sity of California, Berkeley, was born on November 12,1914, in Ningbo, Zhejiang Province, China. He received his B.A.degree in economics in 1940 from National Tsing Hua University in China; his M.A.degree in 1948 and his Ph.D. degree in 1953, both in statistics from University of California, Berkeley.Dr.Chiang was on the U. C. Berkeley faculty for 36 years and has served as Chairman of the Program in Biostatistics, of the Division of Measurement Sciences and of the Faculty of the School of Public Health,and as Co-chairman of the Group of Biostatistics. When he retired in l987, the University honored him with “The Berkeley Citation ” award for his “distinguished achievement.” He was recalled to active duty in 1996. Dr.Chiang has been invited as a visiting professor at the following universities: Harvard; Yale; Pittsburgh; North Carolina; Emory; Michigan; Minnesota; Texas; Vanderbilt; and Washington at Seattle. He has given courses at Peking University, Beijing Medical University and Tongji Medical University, all in China, and at Tunghai University in Taiwan. In addition to his many scientific articles, he has published four books, two of which are about stochastic processes. Three of his books have been translated into Chinese and one into Japanese. Professor Chiang is a Fellow of the American Statistical Association, of the Institute of Mathematical Statistics and of the Royal Statistical Society of London. He has served as a special consultant to several national and international agencies. Professor Chiang is residing with his wife in Berkeley, California. They have two sons and one daughter, and two grandsons.