Meta-analysis is a quantitative method used to integrate multiple research papers to evaluate the impacts of various interventions on different outcomes. Aiming to provide guidance for conducting meta-analysis, this chapter begins with a discussion of data preparation processes, including data management, analysis preparation, and the use of various statistical techniques to deal with missing data. The discussion of effect size assessment and data conversion tools represents robust meta-analytical techniques. This chapter represents strategies for analyzing the data of continuous and dichotomous outcomes as well as specialized statistical techniques like the generic inverse-variance method. Furthermore, this chapter discusses common limitations in meta-analysis, including the impact of low-quality trials and heterogeneity and metabias. Sensitivity analyses and meta-regression are discussed to resolve the heterogeneity and its effects on meta-analysis findings. This chapter provides guidance to present results, with a particular emphasis on building and interpreting forest plots. The chapter ends by highlighting the importance of meta-analysis in synthesizing evidence and directing future research, highlighting its crucial role in evidence-based medicine.