统计学上来说，元分析（meta-analysis，或译作后设分析、集成分析、综合分析、荟萃分析）是指将多个研究结果集成在一起的统计方法。就用途而言，它是文献回顾的新方法。

文献回顾的传统方法是叙事式的，由作者自行挑选觉得重要的前人研究，当各研究结论冲突时，由作者自行判断哪一种结论较具价值。反之，后设分析采用系统化的数据收集和数值分析，让文献回顾更具客观性与证据力。 这些组合后的结果会有助于以更加全面性的证据观察来支持或拒绝某一主题研究的假设，发现该主题迄今研究的不足处，发现各种可能影响效应量的中介因素。

The basic tenet of a meta-analysis is that there is a common truth behind all conceptually similar scientific studies, but which has been measured with a certain error within individual studies. The aim in meta-analysis then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. In essence, all existing methods yield a weighted average from the results of the individual studies and what differs is the manner in which these weights are allocated and also the manner in which the uncertainty is computed around the point estimate thus generated. In addition to providing an estimate of the unknown common truth, meta-analysis has the capacity to contrast results from different studies and identify patterns among study results, sources of disagreement among those results, or other interesting relationships that may come to light in the context of multiple studies. Meta-analysis can be thought of as "conducting research about previous research." Meta-analysis can only proceed if we are able to identify a common statistical measure that is shared among studies, called the effect size, which has a standard error so that we can proceed with computing a weighted average of that common measure. Such weighting usually takes into consideration the sample sizes of the individual studies, although it can also include other factors, such as study quality.