Meta-analysis in psychology is a statistical method that combines the effect sizes from many independent studies on the same question into a single pooled effect. Instead of treating any one experiment as the final word, it weights each study by its precision, so larger and more reliable studies carry more influence. The result is an estimate that is more accurate, and far harder to dismiss, than the scattered findings it draws from.
For a field built on small samples and surprising headlines, this matters enormously. A dozen studies on the same hypothesis can point in different directions purely by chance. Meta-analysis in psychology turns that noisy pile of results into one defensible number, complete with a confidence interval and an honest account of how much the studies disagree.
Why psychology leans so heavily on pooled evidence
Psychology has a structural problem that meta-analysis is unusually well suited to solve. Most individual studies recruit modest samples, which gives them low statistical power and wide margins of error. Two well-run experiments can easily produce opposite conclusions, not because the underlying truth changed, but because each caught a different slice of random variation.
This is the engine behind the replication crisis. When a single striking study fails to repeat, it is tempting to conclude the effect is fake. A better move is to ask what happens when you combine every credible study on the question. By pooling results and weighting them by precision, meta-analysis reveals whether an effect is real but small, genuinely absent, or strongly dependent on context. That shift, from celebrating one decisive study to weighing the whole body of evidence, is exactly why meta-analysis now sits at the top of the evidence hierarchy in psychological science.
The effect size: the common currency that makes pooling possible
You cannot average raw results from studies that used different scales, tasks, and sample sizes. What you can combine is the effect size, a standardized number that expresses the strength of a finding independently of the original measurement units. Two metrics dominate psychology. The first is Cohen's d, a standardized mean difference that reports how far two group means sit apart in standard-deviation units. The second is the correlation coefficient r, used when the question is about association rather than group difference.
The art of preparing a meta-analysis is converting every primary study into the same metric, then attaching a standard error to each one. Studies with tighter standard errors get more weight in the final weighted average. If you are still building intuition for these numbers, our guide to calculating standardized effect sizes walks through the conversions, and the effect size calculator handles the arithmetic for a single comparison.
Examples of meta-analysis in psychology
The method's founding example is also its clearest. In the mid-1970s Gene Glass coined the term meta-analysis, and together with Mary Lee Smith he synthesized hundreds of psychotherapy outcome studies. Their pooled result indicated that the average treated client improved more than roughly 75 percent of comparable untreated people, a conclusion no single trial could have established with the same authority. It reframed a contentious debate about whether therapy worked into a quantitative question about how much.
Other examples show the method's range. Cross-cultural syntheses of conformity experiments pooled decades of replications of the classic Asch line-judgement task to ask whether conformity has weakened over time and whether it differs across cultures. More recently, large coordinated efforts on ego depletion, the idea that self-control draws on a limited resource, found a much smaller effect than earlier optimistic summaries, precisely because they accounted for unpublished and underpowered studies. Each case demonstrates the same lesson: pooling many studies produces a more honest picture than any headline finding.