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Description: Because negative findings have less chance of getting published, available studies tend to be a biased sample. This leads to a higher prevalence of false positives and the inflation of effect sizes to an unknown degree. To see how meta-analyses in education account for publication bias, we surveyed all meta-analyses published in the last three years in the Review of Educational Research and Educational Research Review. The results show that meta-analyses usually neglect publication bias correction even if there is evidence of it. In the minority of studies adjusting for bias, the outdated trim and fill method was used invariably, and none of the meta-analyses based their conclusions on corrected estimates, rendering the adjustment inconsequential. It is argued that state-of-the-art adjustment (e.g., regression-based or selection models) should be carried out by default, yet one needs to take into account the substantial uncertainty inherent in any meta-analytic inference under publication bias. We conclude by providing practical recommendations on how to deal with publication bias.