Sexual selection has been a popular subject within evolutionary biology because of its central role in explaining odd and counterintuitive traits found in nature. Consequently, the literature associated with this field of study became vast, with meta-analytical studies attempting to draw inferences from it. These meta-analyses have now accumulated, varying in scope and quality, thus calling for a synthesis of these syntheses. Here, we conducted a systematic map with a report appraisal of meta-analyses on topics associated with sexual selection, aiming to comprehend the conceptual and methodological gaps in this secondary literature. To further understand these gaps and their potential origins, we also conducted bibliometric analyses that identify the gender and origin of researchers that generated these studies. We included 152 meta-analytical studies in our systematic map as a result of a systematic literature search. We found that most meta-analyses focused on males and on certain animal groups (e.g. birds), indicating severe sex and taxonomic biases. Moreover, the topics in these studies varied immensely, from proximate (e.g. relationship of ornaments with other traits) to ultimate questions (e.g. formal estimates of sexual selection strength), albeit the former were more common. We also observed several common issues in these studies, such as lack of detailed information regarding searches, screening, and analyses, which ultimately impairs the reliability of many of these meta-analyses. In addition, most of the meta-analyses’ authors were men affiliated to institutions from developed countries, pointing to both gender and geographical authorship biases. Many of our findings might simply reflect patterns in the current state of the primary literature and academia, suggesting that our study can serve as an indicator of the issues with the field of sexual selection at large. Still, we provide both conceptual and analytical recommendations to improve future studies in the field of sexual selection, such as to avoid including humans with other animals in meta-analyses, to clarify traits of interest instead of simply using loosely defined lingo, and to properly match studies’ questions and meta-analytical models.