This paper discusses the importance of investigating sources of heterogeneity in meta-analysis to ensure accurate interpretation of results. While meta-analysis is a valuable tool for summarizing evidence, its misuse can lead to misleading conclusions. A key issue is the failure to explore clinical differences between studies, which can significantly affect the results. The paper distinguishes between clinical heterogeneity (differences in study design, patient characteristics, and interventions) and statistical heterogeneity (differences in study outcomes that cannot be explained by clinical factors). It emphasizes that understanding clinical heterogeneity is crucial for interpreting meta-analysis results. The paper provides examples of meta-analyses where exploring heterogeneity was essential. For instance, in a meta-analysis of trials on endoscopic sclerotherapy for cirrhotic patients with oesophageal varices, the results showed substantial statistical heterogeneity, indicating that the trials differed in many aspects, including patient selection and treatment methods. Similarly, in a meta-analysis of serum cholesterol concentration and ischaemic heart disease, the results showed significant heterogeneity, which was partly explained by differences in age at entry and other factors. The paper also discusses the implications of heterogeneity for interpreting the results of meta-analyses. It argues that ignoring clinical heterogeneity can lead to overinterpretation of the overall effect, as the results may not be generalizable to all populations. The paper highlights the importance of investigating the specific clinical differences between studies to better understand the true effect of interventions. The paper concludes that meta-analyses should focus on understanding the sources of heterogeneity rather than relying solely on statistical tests. This approach can improve the scientific and clinical relevance of the results. The paper also notes that other factors, such as publication bias and methodological quality, can contribute to statistical heterogeneity. Overall, the paper emphasizes the need for careful investigation of heterogeneity in meta-analyses to ensure accurate and meaningful conclusions.This paper discusses the importance of investigating sources of heterogeneity in meta-analysis to ensure accurate interpretation of results. While meta-analysis is a valuable tool for summarizing evidence, its misuse can lead to misleading conclusions. A key issue is the failure to explore clinical differences between studies, which can significantly affect the results. The paper distinguishes between clinical heterogeneity (differences in study design, patient characteristics, and interventions) and statistical heterogeneity (differences in study outcomes that cannot be explained by clinical factors). It emphasizes that understanding clinical heterogeneity is crucial for interpreting meta-analysis results. The paper provides examples of meta-analyses where exploring heterogeneity was essential. For instance, in a meta-analysis of trials on endoscopic sclerotherapy for cirrhotic patients with oesophageal varices, the results showed substantial statistical heterogeneity, indicating that the trials differed in many aspects, including patient selection and treatment methods. Similarly, in a meta-analysis of serum cholesterol concentration and ischaemic heart disease, the results showed significant heterogeneity, which was partly explained by differences in age at entry and other factors. The paper also discusses the implications of heterogeneity for interpreting the results of meta-analyses. It argues that ignoring clinical heterogeneity can lead to overinterpretation of the overall effect, as the results may not be generalizable to all populations. The paper highlights the importance of investigating the specific clinical differences between studies to better understand the true effect of interventions. The paper concludes that meta-analyses should focus on understanding the sources of heterogeneity rather than relying solely on statistical tests. This approach can improve the scientific and clinical relevance of the results. The paper also notes that other factors, such as publication bias and methodological quality, can contribute to statistical heterogeneity. Overall, the paper emphasizes the need for careful investigation of heterogeneity in meta-analyses to ensure accurate and meaningful conclusions.