Meta‑analysis can amplify systematic distortions when the underlying literature suffers from publication bias, p‑hacking, or selective reporting; in such cases a well‑conducted single study (or an explicitly bias‑corrected analysis) may provide a more reliable guide. The post explains funnel‑plot asymmetry, 'trim‑and‑fill' correction, and gives concrete topical examples where pooled estimates exceed realistic effects.
— This reframes how media, courts, and policymakers should treat 'the literature says' claims—demanding provenance, bias diagnostics, and robustness maps rather than relying on pooled estimates alone.
Cremieux
2026.04.02
86% relevant
The article critiques how a small, marginal association (ice‑cream intake and lower cardiometabolic risk) from cohort analyses and a cited meta‑analysis was treated as meaningful by the press; it traces how study design choices, lack of robustness checks, and cumulative biases produce misleading pooled results — the same failure mode captured by the existing idea.
Ethan Siegel
2026.04.01
64% relevant
The critique highlights how combining diverse cosmological probes (e.g., supernovae, CMB, BAO) without fully accounting for systematic differences and priors can produce apparent signals — echoing the existing idea that aggregated analyses can mislead if methodological heterogeneity and biases are not exposed.
2026.03.05
90% relevant
The article explicitly critiques how the JAMA Psychiatry meta‑analysis (Kalfas et al., JAMA Psychiatry, July 2025) uses the DESS symptom‑count scale and reports a standardized mean difference (SMD 0.31) that equates to 'one more symptom' — an example of how meta‑analytic choice of outcome and aggregation can produce misleading impressions about clinical importance.
2026.01.04
100% relevant
The article points to specific diagnostics (funnel plots, trim‑and‑fill) and examples (air pollution, mindfulness) as concrete evidence that many meta‑analytic conclusions are upward‑biased by selective publication.