![]() ![]() At this point, one may wonder about the power of the relevant statistical test to detect a difference if there truly was one. Of course, in the recognition task, normal were much better than amnesiacs with correct recognition scores of 13 versus 8, respectively. Rather, normal were a bit better than amnesiacs with an average of 16 versus 14.5 stems completed with studied words, respectively. However, it may appear a bit puzzling that amnesiacs and normal were not totally equivalent with respect to the indirect word stem completion task. It should be clear by now why the finding of no statistically significant difference between amnesiacs and normal in indirect tests was so exciting: All of a sudden there was evidence for memory where it was not expected, but only when the instructions did not stress the fact that the task was a memory task. If the probability of completing such stems with studied words is above base-line, then we observe an effect of prior experience. ![]() In such a task, a person is given a word stem such as "tri." and is asked to complete it with the first word that comes to mind. In contrast, word stem completion would be an indirect measure of memory. This measure is called direct because the remembering person receives explicit instructions to recollect a prior study episode ("please recognize which of these words you have seen before"). An example of a direct memory measure would be recognition performance. Perhaps the most intriguing result of the Warrington and Weiskrantz study was that amnesics and normals differed with respect to direct, but not indirect measures of memory. ![]() It very often takes them weeks to learn where the bathroom is in a new environment, and some of them never seem to learn such things. Amnesics are persons who have very serious long-term memory problems. In a now-classic study, Warrington and Weiskrantz (1970) compared the memory performance of amnesics to normal controls. Let us now start with the simplest possible case, a t-test for independent samples. If you do not yet have your data set (e.g., in the case of an a priori power analysis), then you could simply create an appropriate artificial data set and check the degrees of freedom for this set. As a general rule, therefore, we recommend that you routinely compare the degrees of freedom as specified in G*Power with the degrees of freedom that your statistical analysis program gives you for an appropriate set of data. A very frequent error in performing power analyses with G*Power is to specify incorrect degrees of freedom. ¡Descarga GPOWER Tutorial y más Resúmenes en PDF de Diseños y Grupos solo en Docsity!GPOWER Tutorial Before we begin this tutorial, we would like to give you a general advice for performing power analyses. ![]()
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