Statistical significance is concerned with whether a research result is due to chance or sampling variability; practical significance is concerned with whether the result is useful in the real world. A growing awareness of the limitations of null hypothesis significance tests has led to a search for ways to supplement these procedures. A variety of supplementary measures of effect magnitude have been proposed. The use of these procedures in four APA journals is examined, and an approach to assessing the practical significance of data is described.

Provides detailed coverage of the designs and techniques that have the greatest potential use in behavioral research for the graduate student, statistician, researcher. Introduces the concept of building block designs and describes complex experimental designs in terms of simple building block designs. 550 pages. Clothbound.

Researchers want to answer three basic questions: (a) Is an observed effect real or should it be attributed to chance? (b) If the effect is real, how large is it? and (c) Is the effect large enough to be useful? The first question concerning whether chance is a viable explanation for an observed effect is usually addressed with a null hypothesis significance test. A null hypothesis significance test tells us the probability of obtaining the effect or a more extreme effect if the null hypothesis is true. A significance test does not tell us how large the effect is or whether the effect is important or useful. Unfortunately, all too often the primary focus of research is on rejecting a null hypothesis and obtaining a small p value. The focus should be on what the data tell us about the phenomenon under investigation. This is not a new idea. Critics of significance testing have been saying it for years. For example, Frank Yates (1951), a contemporary of Ronald Fisher, observed that the use of the null hypothesis significance test has caused scientific research workers to pay undue attention to the results of the tests of significance they perform on their data, and too little to the estimates of the magnitude of the effects they are investigating. . . . The emphasis on tests of significance, and the consideration of the results of each experiment in isolation, have had the unfortunate consequence that scientific workers have often regarded the execution of a test of significance on an experiment as the ultimate objective. (pp. 32-33) The view that an emphasis on null hypothesis significance tests detracts researchers from the main business of science-interpreting the outcome of research, theory development, and so on-is shared by many contemporary

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