Which statement best describes meta-analysis?

Which statement best describes meta-analysis?

• A. It averages p-values across studies to determine significance.
• B. It is a qualitative review of the literature.
• C. It uses a simple average of reported results without standardization.
• D. It is a statistical procedure for assimilating research findings by combining effect sizes from multiple studies.

Answer: (D)

Meta-analysis is a statistical procedure for assimilating research findings by combining effect sizes from multiple studies. Instead of simply listing study results, it converts each study’s result into a common metric (such as a standardized mean difference or an odds ratio) and weighting each study by how precise its estimate is, often tied to its sample size or variance. This approach yields a single overall estimate of the effect that reflects the magnitudes and directions observed across studies, while also showing how consistent the findings are. A method that averages p-values across studies isn’t appropriate because p-values convey information about statistical significance under a particular sample size and design, not about the size or direction of effects, and they don’t combine cleanly across diverse studies. A qualitative, narrative review describes findings without a formal quantitative synthesis, which lacks the numeric estimate meta-analysis provides. Simply averaging reported results without standardization ignores different scales and variances and can misrepresent the true overall effect. Thus, meta-analysis stands out as a rigorous way to synthesize research by pooling standardized effect sizes from multiple studies to produce a more precise and generalizable estimate of the effect.

Meta-analysis is a statistical procedure for assimilating research findings by combining effect sizes from multiple studies. Instead of simply listing study results, it converts each study’s result into a common metric (such as a standardized mean difference or an odds ratio) and weighting each study by how precise its estimate is, often tied to its sample size or variance. This approach yields a single overall estimate of the effect that reflects the magnitudes and directions observed across studies, while also showing how consistent the findings are.

A method that averages p-values across studies isn’t appropriate because p-values convey information about statistical significance under a particular sample size and design, not about the size or direction of effects, and they don’t combine cleanly across diverse studies. A qualitative, narrative review describes findings without a formal quantitative synthesis, which lacks the numeric estimate meta-analysis provides. Simply averaging reported results without standardization ignores different scales and variances and can misrepresent the true overall effect.

Thus, meta-analysis stands out as a rigorous way to synthesize research by pooling standardized effect sizes from multiple studies to produce a more precise and generalizable estimate of the effect.