Meta-Analysis

Meta-analysis is a statistical technique that quantitatively synthesizes the results of multiple independent studies addressing the same question, producing a pooled estimate of an effect size with greater statistical power and precision than any individual study. It is one of the scientific method's most important error-correction tools: by aggregating results across studies with different designs, populations, and methodologies, meta-analysis can reveal consistent effects masked by individual study noise, detect heterogeneity that indicates the effect depends on context, and identify publication bias through funnel plot asymmetry. The technique was developed in the 1970s (Gene Glass coined the term in 1976) and became the methodological backbone of evidence-based medicine. Its limitations are equally important: garbage in, garbage out — a meta-analysis of low-quality studies produces a precise estimate of the wrong answer. The replication crisis has revealed that many meta-analyses in psychology and medicine synthesized studies with common methodological flaws (publication bias, p-hacking), producing confidently wrong pooled estimates. Pre-registered meta-analyses using raw data from registered studies are the current best practice for avoiding this failure mode.