The results of research on a specific question differ across studies, some to a small extent and some to a large extent. Meta-analysis is a way to statistically combine and summarize the results of different studies so as to obtain a pooled or summary estimate that may better represent what is true in the population. Meta-analysis can be conducted for a variety of statistics, including means, mean differences, standardized mean differences, proportions, differences in proportions, relative risks, odds ratios, and others. The results of metaanalysis are presented in forest plots. This article explains why meta-analysis may be necessary, how a systematic review is conducted to identify studies for meta-analysis, and how to interpret the various elements in a forest plot. Brief discussions are provided about important concepts relevant to meta-analysis, including heterogeneity, subgroup analyses, sensitivity analyses, fixed effect and random effects meta-analyses, and the detection of publication bias. Other procedures briefly explained include meta-regression analysis, pooled analysis, individual participant data meta-analysis, and network meta-analysis. The limitations of meta-analysis are also discussed.