5 6 2 3 5 5 7 6 8 5 4 7 3 5 6 7 6 6 2 9 8 7 6 9 7 3 9 5 7 6 normal 8 normal 8 Consider a within-subject experimental design with 2 conditions. The experimenter ran both a correlated pairs t-test and (incorrectly) an independent samples t-test. The experimenter found the correlated pair t-test was significant whereas the independent samples test was not. The experimenter conlcuded that this was because the mean difference score across subjects was greater than the difference between the means of the conditions. Is this the correct conclusion? The two mean differences are the same. The correlated t test controlled for differences between subjects and thus reduced the standard error of the difference between the two means false yes true no Correlated pairs t-tests generally have more power than independent sample t-tests because the former usually have smaller standard errors of the difference between means making the t-value bigger. By using each subject as its own control, the standard error is lower. true True false False Increasing the correlation between measures: All else being equal, the higher the correlation, the lower the standard error and therefore the higher the absolute value of t. true increases the absolute value of t. false decreases the absolute value of t. false has no effect on the t. The null hypothesis in a correlated pairs t-test is that the population mean difference score is 0. If the mean difference score is not 0, then the means of the two conditions will not be equal. true True false False Using these data given, what is the t statistic using a correlated pairs t-test 2 2 0.02 Compute dependent t-test t value for these two variables. showCorrectAnswer