This tutorial explains standardized mean differences (SMD) as an effect measure in meta-analyses. SMD is used when studies assess the same outcome but use different scales. It standardizes results to a common effect size measure, allowing comparison across studies. The SMD is calculated by dividing the mean difference (MD) between treatment and control groups by the pooled sample standard deviation (SD) of the outcome at a specific follow-up time point. The tutorial provides an example using data from Ortiz-Alonso et al., demonstrating how to compute SMD using pooled SDs at baseline and posttreatment time points. It also discusses how to interpret SMDs, including re-expressing them in familiar units for better clinical understanding. Common pitfalls in using SMDs are outlined, such as unnecessary data standardization, using standard errors (SE) instead of SDs, combining baseline and posttreatment effects, effect size direction, and failing to interpret SMDs. The tutorial emphasizes the importance of transparency and reproducibility in evidence synthesis, recommending detailed reporting of data standardization methods and re-expression. It also highlights the use of SMDs in Cochrane reviews and provides resources for further learning, including statistical packages and a book on meta-analysis. The tutorial concludes with recommendations for promoting reproducibility and clarity in reporting SMDs.This tutorial explains standardized mean differences (SMD) as an effect measure in meta-analyses. SMD is used when studies assess the same outcome but use different scales. It standardizes results to a common effect size measure, allowing comparison across studies. The SMD is calculated by dividing the mean difference (MD) between treatment and control groups by the pooled sample standard deviation (SD) of the outcome at a specific follow-up time point. The tutorial provides an example using data from Ortiz-Alonso et al., demonstrating how to compute SMD using pooled SDs at baseline and posttreatment time points. It also discusses how to interpret SMDs, including re-expressing them in familiar units for better clinical understanding. Common pitfalls in using SMDs are outlined, such as unnecessary data standardization, using standard errors (SE) instead of SDs, combining baseline and posttreatment effects, effect size direction, and failing to interpret SMDs. The tutorial emphasizes the importance of transparency and reproducibility in evidence synthesis, recommending detailed reporting of data standardization methods and re-expression. It also highlights the use of SMDs in Cochrane reviews and provides resources for further learning, including statistical packages and a book on meta-analysis. The tutorial concludes with recommendations for promoting reproducibility and clarity in reporting SMDs.