The Brookbush Institute Publishes a NEW Glossary Term: 'Meta-analysis (MA)'

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NEW YORK, NY, UNITED STATES, August 19, 2025 /EINPresswire.com/ -- - Excerpt from Glossary Term: Meta-analysis (MA)
- Additional Glossary Term: Systematic Review
- Related Certification: Human Movement Specialist (HMS) Certification


DEFINITION
Meta-analysis (MA) employs statistical methods to aggregate effect sizes from multiple studies, intending to estimate the overall effect magnitude, determine the probable effect direction, or assess statistical significance. Meta-analyses are typically built upon a systematic review framework, which defines the criteria for study inclusion and ensures methodological transparency. (See: How to Perform a Meta-analysis below.) In this sense, meta-analysis is best understood as a statistical tool that may contribute additional insight to a well-conducted systematic review. However, it is not a review in itself.

PURPOSE AND FUNCTION
The primary utility of a meta-analysis lies in resolving differences between high-quality randomized controlled trials (RCTs) (with similar design) that report conflicting findings. When applied correctly, a meta-analysis may enhance statistical power, refine estimates of effect size, or clarify the likely direction of effect. Its value is most evident when the included studies are sufficiently similar in design and outcome measures, and when a definitive conclusion cannot be drawn from individual trials alone.

WHAT A META-ANALYSIS IS NOT
Meta-analysis is not original research, nor is it a “superior” form of evidence. It is a form of secondary analysis (an average of averages) and should not be interpreted as more valid than the underlying primary studies. Although this form of synthesis can be powerful, considering a meta-analysis superior to the original data it summarizes is analogous to treating a reviewer’s movie rating as more authoritative than the film itself. As noted above, meta-analyses have a distinct purpose and function, but they also introduce new risks: additional layers of bias and error, statistical distortion, and misinterpretation.

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