Abstract
Statistical tests of significance tend to produce Type I error probabilities far in excess of the reported "alphas"; when more than one significance test is conducted on a data set. Since this condition is almost always the case in communication research, most reported alpha levels are little more than fiction as they relate to the occurrence of the Type I errors they are supposed to index and monitor. The calculation of an alpha percentage (α%) allows an estimation of the number of Type I errors being reported as "significant,"; suggests a rational basis for where to set the nominal alpha level for a given data set in order to obtain any specified ratio of fictional to "real"; results, and provides a general method of controlling Type I error rates since it is applicable across all tests of significance.