Summary
Contents
Subject index
This book is a simple introduction for nonstatisticians to power analysis and sample size determination. It clearly illustrates why sample sizes need to be sufficiently large, so that the experiment has good power properties and hence low type II error rates. The authors introduce a simple technique of statistical power analysis that allows researchers to compute approximate sample sizes and power for a wide variety of research designs. Because the same technique is used with only slight modifications for different statistical tests, researchers can easily compare the sample sizes required by different designs and tests to make cost-effective decisions in planning a study. These comparisons, emphasized throughout the book, demonstrate some important principles of design, measurement, and analysis that are not given sufficient emphasis in other texts.
General Concepts
General Concepts
From this point on, the focus of attention is on that one step in the scientific method that is labeled Hypothesis Testing in Figure 1.0. The scientific method requires that the researchers proposing a theory (e.g., drinking coffee may affect health) put that theory to an empirical test. Statistical hypothesis testing is one such formalized empirical test, in structure analogous to the Anglo-Saxon system of trial by jury.
The basic overall principle is that the researchers' theory is considered false until demonstrated beyond reasonable doubt to be true. Until the evidence demonstrates the dangers or benefits of drinking coffee, we assume that drinking coffee makes no difference to health. This is expressed as an assumption that the null hypothesis, the contradiction of the ...
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