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The practical guide behind this tool: when it applies, how to read the output, and the traps that make it say the wrong thing.
Standard-library analysis: pool effect sizes across studies. Map one row per study: a study label, the effect estimate, and either its standard error or its 95% confidence interval, and get the forest plot, the fixed-effect (inverse-variance) and random-effects (DerSimonian-Laird) pooled estimates side by side, the heterogeneity diagnostics that decide which of the two means anything (Cochran's Q, I-squared, tau-squared, plus a prediction interval for the next study), optional subgroup pooling with a between-subgroup Q test, and a funnel plot with Egger's regression for small-study effects, reported with an explicit statement of how little power that test has at your study count.
Interactive scatter visualization
Interactive table visualization
Interactive table visualization
Interactive table visualization
Interactive scatter visualization
Interactive table visualization
Interactive table visualization
Plain-English reading of the results and what to do next.
Pool the effect sizes reported by a set of studies into one estimate with a confidence interval
See our FAQ for pricing, privacy and how the analysis works.
MCP Analytics. Meta-Analysis & Forest Plot. Free tool. https://mcpanalytics.ai/free/standard_meta_analysis. Accessed [date you used it].
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