Strengthening some common multiple test procedures for discrete data.
This paper proposes modification of some commonly used multiple test procedures for testing problems that arise when the underlying distributions are discrete. These procedures have been shown as conservative because the exact nominal levels are unattainable. The amended procedures can achieve actual type I errors much closer to the nominal levels, thus giving rise to more powerful tests.
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Rhône-Poulenc Rorer Central Research, Horsham, PA 19044.
D000818:Animals; D003627:Data Interpretation, Statistical; D051379:Mice; D015233:Models, Statistical; D009374:Neoplasms, Experimental / Q000139:chemically induced; D011336:Probability