What does meta-analysis primarily involve?

Meta-analysis primarily involves combining results from multiple studies to arrive at a more comprehensive understanding of a specific research question or hypothesis. This statistical method takes the findings of various independent studies that address the same question and analyzes their results collectively. By aggregating data, meta-analysis enhances the statistical power and provides more reliable estimates of effects, which can refine conclusions that individual studies might suggest.

This approach is particularly valuable in fields like medicine, psychology, and social sciences, where research studies can yield varied results due to differences in methodologies, sample sizes, and contexts. By synthesizing these results, meta-analysis allows researchers to identify patterns, assess the strength of evidence, and even address inconsistencies across studies, contributing to evidence-informed practice.

Other options do not accurately reflect the primary focus of meta-analysis. For instance, assessing qualitative data is more aligned with qualitative research methodologies and not the statistical aggregation that meta-analysis entails. Conducting original research refers to gathering new data rather than synthesizing existing studies. Lastly, reviewing literature without statistical evaluation would be more akin to a traditional literature review, which lacks the statistical rigor and synthesis characteristic of a meta-analysis.