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A method of using information from previous studies is applied to the problem of estimating treatment effectiveness of a preventive agent in terms of percentage reduction. The Bayesian statistical method allows the use of such prior information in analyzing the results of a study. [ABSTRACT FROM AUTHOR]

Standard statistical literature has been developed almost entirely in terms of independent observations obtained by simple random sampling (SRS). The statistical tests and confidence intervals one finds in textbooks and in journal articles are based on the assumption that the sample observations were selected independently, at random. Although this assumption either appears in "fine print" or not at all, it constitutes the basis of all confidence intervals and tests of hypotheses, their validity is based on it. The ubiquity of the SRS assumption in statistical theory can be explained by the basic nature of SRS and by its facilitation of interesting theoretical results. On the other hand, most social research, especially survey work, is in fact carried out by means of complex sample designs. Simple random selection of human populations is a rare phenomenon, limited to small and confined populations. Socially important human populations are usually large and widely scattered, so that a simple random selection of them would prove in most cases to be uneconomical and impractical.

The article discusses the use of confidence bands to evaluate the reliability of a difference between two test scores. Over the past ten years, testing authorities have increasingly recommended the use of confidence bands in plotting test score profiles. When a profile of scores is presented in the form of percentile rank bands, attention is naturally drawn to the element of unreliability in the score information. The theory underlying the technique of comparing bands is relatively simple. If a test has a standard error of measurement and the confidence interval is to be obtained for an examinee's true scare, the quantity test quantity is added to and subtracted from his observed score. The resultant interval can be converted into percentile rank terms by referring the limits to the appropriate normative tables. The interval is based on the assumption that errors of measurement are normally distributed with constant at all true score levels. It is shown here that a requirement of non-overlap may result in a failure to recognize large true-score differences when they exist.

This article discusses the results of a research study conducted to establish a procedure for calculating a confidence interval to determine the efficacy of proposed anticariogenic agents. The researchers state that most studies of anticariogenic agents base their success on per cent reduction of caries incidence.