The "Q" test is regularly used in meta-analysis to examine variation in effect sizes. However, the assumptions of "Q" are unlikely to be satisfied in practice prompting methodological researchers to conduct computer simulation studies examining its statistical properties. Narrative summaries of this literature are available but a quantitative synthesis of study findings for using the "Q" test has not appeared. We quantitatively synthesized estimated Type I error rates and power values of a sample of computer simulation studies of the "Q" test. The results suggest that "Q" should not be used for standardized mean difference effect sizes like Hedges' g unless the number of studies and primary study sample sizes are at least 40. Use of the Fisher's r-to-z transformed effect size, on the other hand, resulted in "Q" performing well in almost all conditions studied. We summarize our findings in a table that provides guidelines for using this important test.