A generalization of Student's "t" statistic, called Hotelling's "t"-squared statistic, allows for the testing of hypotheses on multiple (often correlated) measures within the same sample. For instance, a researcher might submit a number of subjects to a personality test consisting of multiple personality scales (e.g. the Minnesota Multiphasic Personality Inventory). Because measures of this type are usually positively correlated, it is not advisable to conduct separate univariate "t"-tests to test hypotheses, as these would neglect the covariance among measures and inflate the chance of falsely rejecting at least one hypothesis (Type I error). In this case a single multivariate test is preferable for hypothesis testing. Fisher's Method for combining multiple tests with "alpha" reduced for positive correlation among tests is one. Another is Hotelling's "T" statistic follows a "T" distribution. However, in practice the distribution is rarely used, since tabulated values for "T" are hard to find. Usually, "T" is converted instead to an "F" statistic.