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Unformatted text preview: becomes our random variable, which we call our "statistic". We can now apply the t-test or z-test interpretation of probability. We are now able to determine the probability of a randomly chosen sample mean having a value at least as extreme as our original sample mean. Note that we are implicitly assuming that the null hypothesis is true. This probability is our p-value which we apply to the original problem. Remember that, in the t-tests for differences in means, there is a condition of equal population variances that must be examined. One way to test for possible differences in variances is to do an F test. However, the F test is very sensitive to violations of the normality condition ; i.e., if populations appear not to be normal, then the F test will tend to reject too often the null of no differences in population variances....
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- Spring '08