The article discusses the importance of assessing heterogeneity in meta-analyses and introduces the $ I^{2} $ statistic as a better measure of inconsistency. Systematic reviews and meta-analyses provide reliable evidence for medical decisions, but their conclusions depend on the consistency of study results. When results are inconsistent, the reliability of the meta-analysis is reduced. Traditional tests for heterogeneity, such as Cochran's Q, are limited in their ability to detect true heterogeneity, especially with small numbers of studies. The $ I^{2} $ statistic quantifies the proportion of variation in study results due to heterogeneity rather than chance. It is calculated as $ I^{2} = 100\% \times (Q - df)/Q $, where Q is Cochran's statistic and df is the degrees of freedom. $ I^{2} $ values range from 0% (no heterogeneity) to 100% (maximum heterogeneity). The article provides examples of $ I^{2} $ values from various meta-analyses, showing that high $ I^{2} $ values do not always indicate significant heterogeneity. $ I^{2} $ is preferred over traditional heterogeneity tests because it provides a more intuitive and direct measure of inconsistency. It can be used across different types of studies and outcomes, and is not dependent on the number of studies included. The article also discusses the limitations of $ I^{2} $, such as the need for careful interpretation and the importance of considering clinical implications. The $ I^{2} $ statistic is recommended for assessing heterogeneity in meta-analyses, as it provides a more reliable measure of inconsistency than traditional statistical tests.The article discusses the importance of assessing heterogeneity in meta-analyses and introduces the $ I^{2} $ statistic as a better measure of inconsistency. Systematic reviews and meta-analyses provide reliable evidence for medical decisions, but their conclusions depend on the consistency of study results. When results are inconsistent, the reliability of the meta-analysis is reduced. Traditional tests for heterogeneity, such as Cochran's Q, are limited in their ability to detect true heterogeneity, especially with small numbers of studies. The $ I^{2} $ statistic quantifies the proportion of variation in study results due to heterogeneity rather than chance. It is calculated as $ I^{2} = 100\% \times (Q - df)/Q $, where Q is Cochran's statistic and df is the degrees of freedom. $ I^{2} $ values range from 0% (no heterogeneity) to 100% (maximum heterogeneity). The article provides examples of $ I^{2} $ values from various meta-analyses, showing that high $ I^{2} $ values do not always indicate significant heterogeneity. $ I^{2} $ is preferred over traditional heterogeneity tests because it provides a more intuitive and direct measure of inconsistency. It can be used across different types of studies and outcomes, and is not dependent on the number of studies included. The article also discusses the limitations of $ I^{2} $, such as the need for careful interpretation and the importance of considering clinical implications. The $ I^{2} $ statistic is recommended for assessing heterogeneity in meta-analyses, as it provides a more reliable measure of inconsistency than traditional statistical tests.