This tutorial by Daniel Gallardo-Gómez, Rachel Richardson, and Kerry Dwan focuses on standardized mean differences (SMD) as effect measures in meta-analyses. It explains what SMDs are, when they should be used, how to compute and interpret them, and common errors made in evidence synthesis. SMDs are used when studies assess the same outcome but use different scales, allowing for the pooling of data from various studies. The tutorial provides a detailed explanation of the calculation of SMDs, including the use of pooled sample standard deviations (SDs) at specific follow-up time points. It also offers practical examples and step-by-step calculations to illustrate the process. Additionally, the tutorial discusses methods for interpreting SMDs, such as re-expressing them using familiar instruments or effect size rules of thumb. Common pitfalls, such as unnecessary data standardization, using standard errors instead of SDs, and combining change from baseline and posttreatment effects, are also addressed. The authors recommend providing detailed information about scale directions, effect size values, and data standardization methods to enhance transparency and reproducibility. The tutorial is accompanied by practical exercises and resources, including a micro-learning module and statistical software packages for SMD calculations.This tutorial by Daniel Gallardo-Gómez, Rachel Richardson, and Kerry Dwan focuses on standardized mean differences (SMD) as effect measures in meta-analyses. It explains what SMDs are, when they should be used, how to compute and interpret them, and common errors made in evidence synthesis. SMDs are used when studies assess the same outcome but use different scales, allowing for the pooling of data from various studies. The tutorial provides a detailed explanation of the calculation of SMDs, including the use of pooled sample standard deviations (SDs) at specific follow-up time points. It also offers practical examples and step-by-step calculations to illustrate the process. Additionally, the tutorial discusses methods for interpreting SMDs, such as re-expressing them using familiar instruments or effect size rules of thumb. Common pitfalls, such as unnecessary data standardization, using standard errors instead of SDs, and combining change from baseline and posttreatment effects, are also addressed. The authors recommend providing detailed information about scale directions, effect size values, and data standardization methods to enhance transparency and reproducibility. The tutorial is accompanied by practical exercises and resources, including a micro-learning module and statistical software packages for SMD calculations.