/explanation/index.html Background material for interpreting results and understanding the package’s small-sample stance. Pages Interpretation Release Status FAQ Hugo en-gb /explanation/interpretation/index.html Mon, 01 Jan 0001 00:00:00 +0000 /explanation/interpretation/index.html marketbayesmeta produces review artefacts, not automatic decisions. Reportability A completed run is not automatically reportable. Check: run_status.reportable readiness.csv diagnostics.csv prior_diagnostics.csv sensitivity outputs analysis_report.md Future true effect future_true_effect_summary.csv describes the latent true effect for a comparable future study. It excludes measurement error and is not a prediction for the next observed study estimate. /explanation/release-status/index.html Mon, 01 Jan 0001 00:00:00 +0000 /explanation/release-status/index.html The current internal release candidate is 0.3.0. Suitable use 0.3.0 is suitable for supervised Data Science analyst workflows where outputs are reviewed before reporting. It is not intended for unattended production reporting. Validation snapshot Validated locally on June 3, 2026 using the repository virtual environment: /explanation/faq/index.html Mon, 01 Jan 0001 00:00:00 +0000 /explanation/faq/index.html Does the library fit Bayesian random-effects models with PyMC? Yes. The default model is a Bayesian normal-normal random-effects meta-analysis in PyMC. Random effects are a sensible default for comparable marketing measurement studies because campaigns, markets, periods, execution, and measurement designs often differ. When should studies not be pooled? Do not pool studies just because they share a broad label such as “sales uplift” or “awareness”. Pool only when the estimand, scale, population, measurement window, and study design are comparable enough for a pooled effect to mean something.