The article advocates for the routine reporting of prediction intervals in meta-analyses to provide a more comprehensive understanding of the uncertainty surrounding intervention effects. Prediction intervals, which reflect the expected range of true effects in similar studies, are compared to traditional measures like τ² and I², which are less intuitive for clinical interpretation. The authors analyzed 479 statistically significant meta-analyses from the Cochrane Database of Systematic Reviews (2009-2013) with heterogeneity (I² > 0) and found that in 72.4% of these analyses, the 95% prediction interval suggested that the intervention effect could be null or even opposite to the point estimate. In 20.3% of the analyses, the prediction interval indicated a complete opposite effect. The authors conclude that prediction intervals should be routinely reported to enhance the clinical utility and interpretability of meta-analyses, allowing for more informed decision-making.The article advocates for the routine reporting of prediction intervals in meta-analyses to provide a more comprehensive understanding of the uncertainty surrounding intervention effects. Prediction intervals, which reflect the expected range of true effects in similar studies, are compared to traditional measures like τ² and I², which are less intuitive for clinical interpretation. The authors analyzed 479 statistically significant meta-analyses from the Cochrane Database of Systematic Reviews (2009-2013) with heterogeneity (I² > 0) and found that in 72.4% of these analyses, the 95% prediction interval suggested that the intervention effect could be null or even opposite to the point estimate. In 20.3% of the analyses, the prediction interval indicated a complete opposite effect. The authors conclude that prediction intervals should be routinely reported to enhance the clinical utility and interpretability of meta-analyses, allowing for more informed decision-making.