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For instance, in a test for a drug reducing blood pressure the colour of the patients’ eyes would probably be irrelevant, but their resting diastolic blood pressure could well provide a basis for selecting the pairs. The likeness within the pairs applies to attributes relating to the study in question. The more alike they are, the more apparent will be any differences due to treatment, because they will not be confused with differences in the results caused by disparities between members of the pair. As the aim is to test the difference, if any, between two types of treatment, the choice of members for each pair is designed to make them as alike as possible. The second case of a paired comparison to consider is when two samples are chosen and each member of sample 1 is paired with one member of sample 2, as in a matched case control study. Applying this method to the data of Table 7.1, the calculator method (using a Casio fx-350) for calculating the standard error is: If a log transformation is successful use the usual t test on the logged data. Transformations that render distributions closer to Normality often also make the standard deviations similar. However, it should not be used indiscriminantly because, if the standard deviations are different, how can we interpret a nonsignificant difference in means, for example? Often a better strategy is to try a data transformation, such as taking logarithms as described in Chapter 2. The unequal variance t test tends to be less powerful than the usual t test if the variances are in fact the same, since it uses fewer assumptions. Many statistical packages now carry out this test as the default, and to get the equal variances I statistic one has to specifically ask for it. In this case one should round to the nearest integer. It can produce a degree of freedom which is not an integer, and so not available in the tables.
#Log transformation hypothesis test calculator how to
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