Meta-analysis is the statistical combination of results from two or more separate studies. Potential advantages of meta-analyses include an increase in power, an improvement in precision, the ability to answer questions not posed by individual studies, and the opportunity to settle controversies arising from conflicting claims. However, they also have the potential to mislead seriously, particularly if specific study designs, within-study biases, variation across studies, and reporting biases are not carefully considered. It is important to be familiar with the type of data (e.g. dichotomous, continuous) that result from measurement of an outcome in an individual study, and to choose suitable effect measures for comparing intervention groups. Most meta-analysis methods are variations on a weighted average of the effect estimates from the different studies. Variation across studies (heterogeneity) must be considered. Random-effects meta-analyses allow for heterogeneity by assuming that underlying effects follow a normal distribution. Various judgments are required in the process of preparing a meta-analysis. Especially, quality assessment of randomized controlled trial is essential. There are several methods to assess the methodological quality of clinical trials, including scales, individual markers, and checklists. Analyzing the quality of studies makes the results of meta-analysis more reliable. Sensitivity analyses should be used to examine whether overall findings are robust to potentially influential decisions.