# The Future of Psychiatry Will Become What They Behold.

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**ABSTRACT:**This is a handbook for behavioral or biosocial scientists who use statistical inference. It is assumed only that the reader knows how to proceed to perform a test of statistical significance. The first chapter presents a general description of statistical hypotheses testing. There are described four parameters of statistical inference: power, significance criterion (a), sample size (n) and effect size (ES). Chapters 2 to 10 present different statistical tests. They are organized in the following way: 1. The test is introduced; 2. the ES index is described and discussed; 3. the power tables are presented together with the method of their use; 4. the sample size tables are presented and the method of their use is described. Chapters 2, 3, 4, 5, 6 and 8 are equipped with numerous illustrative examples. One can find the following tests: The t test for means (ch. 2); the Pearson product-moment correlation coefficient r s (ch. 3); testing of hypotheses concerning differences between population correlation coefficients (ch. 4); the sign test and the test that some defined characteristic is one- half (ch. 5); testing of hypotheses concerning differences between independent population proportions (ch. 6); chi-square tests (ch. 7); the analysis of variance (ch. 8); multiple regression and correlation analysis (ch. 9); set correlation and multivariate methods (ch. 10). The major changes in comparison with the first edition are: there is a new chapter 10 and a new chapter 11 treating the power analysis in more integrated form: effect size, psychometric reliability and the efficacy of “qualifying” dependent variables. Also, the references have been updated.01/1988; L. Erlbaum Associates., ISBN: 9780805802832 - Controlled Clinical Trials 03/1990; 11(1):67-9. DOI:10.1016/0197-2456(90)90033-X
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**ABSTRACT:**The relative benefit of an active treatment over a control is usually expressed as the relative risk, the relative risk reduction, or the odds ratio. These measures are used extensively in both clinical and epidemiological investigations. For clinical decision making, however, it is more meaningful to use the measure "number needed to treat." This measure is calculated on the inverse of the absolute risk reduction. It has the advantage that it conveys both statistical and clinical significance to the doctor. Furthermore, it can be used to extrapolate published findings to a patient at an arbitrary specified baseline risk when the relative risk reduction associated with treatment is constant for all levels of risk.BMJ Clinical Research 03/1995; 310(6977):452-4. DOI:10.1136/bmj.310.6977.452 · 14.09 Impact Factor