This study examines the effectiveness of funnel plots in detecting bias in meta-analyses. Funnel plots, which plot effect estimates against sample size, are used to assess the validity of meta-analyses. The researchers identified pairs of meta-analyses and large trials from Medline and Cochrane databases to compare results and assess funnel plot asymmetry. They found that in four out of eight pairs, discordance between meta-analyses and large trials was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but not in any concordant pairs. In 38% of journal meta-analyses and 13% of Cochrane reviews, funnel plot asymmetry indicated potential bias. The study concludes that a simple analysis of funnel plots can be a useful test for detecting bias in meta-analyses, but the results should be interpreted with caution, especially when based on small trials.This study examines the effectiveness of funnel plots in detecting bias in meta-analyses. Funnel plots, which plot effect estimates against sample size, are used to assess the validity of meta-analyses. The researchers identified pairs of meta-analyses and large trials from Medline and Cochrane databases to compare results and assess funnel plot asymmetry. They found that in four out of eight pairs, discordance between meta-analyses and large trials was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but not in any concordant pairs. In 38% of journal meta-analyses and 13% of Cochrane reviews, funnel plot asymmetry indicated potential bias. The study concludes that a simple analysis of funnel plots can be a useful test for detecting bias in meta-analyses, but the results should be interpreted with caution, especially when based on small trials.