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The Stouffer method of adding Zs is the most familiar and widely used method for pooling the significance levels of multiple hypothesis tests. However, this method requires the assumption under the null hypothesis that the outcomes of the tests are statistically independent. A method for pooling the significance of nonoverlapping, or “doubly nonindependent,” correlations within a single study is presented in this article. Monte Carlo studies demonstrate that the method controls the Type I error rate even with small samples and does so better than (a) ignoring nonindependence and using Stouffer's method or (b) adjusting for nonindependence in the dependent variable set only using Strube's method (M. J. Strube, 1985).