TO HOW MANY SIMULTANEOUS HYPOTHESIS TESTS CAN NORMAL, STUDENT’S t OR BOOTSTRAP CALIBRATION BE APPLIED?

In the analysis of microarray data, and in some other contemporary statistical problems, it is not uncommon to apply hypothesis tests in a highly simultaneous way. The number, ν say, of tests used can be much larger than the sample sizes, n, to which the tests are applied, yet we wish to calibrate the tests so that the overall level of the simultaneous test is accurate. Often the sampling distribution is quite different for each test, so there may not be an opportunity for combining data across samples. In this setting, how large can ν be, as a function of n, before level accuracy becomes poor? In the present paper we answer this question in cases where the statistic under test is of Student’s t type. We show that if either normal or Student’s t distribution is used for calibration then the level of the simultaneous test is accurate provided log ν increases at a strictly slower rate than n as n diverges. If log ν and n diverge at the same rate then asymptotic level accuracy requires the average value of standardised skewness, taken over all distributions to which the tests are applied, to converge to zero as n increases. On the other hand, if bootstrap methods are used for calibration then significantly larger values of ν are feasible; we may choose log ν almost as large as n and still achieve asymptotic level accuracy, regardless of the values of standardised skewness. It seems likely that similar conclusions hold for statistics more general than the Studentised mean, and that the upper bound of n, in the case of bootstrap calibration, can be increased.