Meta-analyses often estimate heterogeneity between studies, but these estimates can have large uncertainty, which must be considered when interpreting results. The extent of statistical heterogeneity influences decisions about combining studies, treatment applicability, and risk factors. However, the uncertainty in heterogeneity estimates is often overlooked, leading to potential misinterpretations. The I² statistic is commonly used to measure heterogeneity, but it has limitations, including low statistical power and large confidence intervals, especially with small numbers of studies. Confidence intervals should be considered when interpreting heterogeneity. A study of 1011 Cochrane meta-analyses and 50 genetic risk factor meta-analyses showed that 95% confidence intervals for I² often span a wide range of heterogeneity, making it difficult to draw conclusions about the true level of heterogeneity. This uncertainty is particularly significant when I² is estimated at 0%, as the upper confidence interval may still indicate high heterogeneity. The I² statistic is not precise, and 95% confidence intervals should always be provided. The clinical implications of this uncertainty are significant, and decisions about homogeneity or heterogeneity should be made cautiously. Performance indicators are increasingly used to monitor the quality of care in primary care settings. These indicators help assess the quality of services provided by primary care doctors and identify areas for improvement. Performance indicators are based on routinely collected data, electronic medical records, and sometimes surveys. In the UK, performance indicators have been introduced to link performance to general practitioners' pay, and public access to performance data is becoming more common. However, there are challenges in comparing performance indicators across different health systems and countries due to differences in data collection and coding standards. Standardization of clinical data and performance indicators is needed for meaningful international comparisons. While performance indicators have potential adverse consequences, such as overtreatment or neglect of certain areas, they are essential for quality improvement and performance management in healthcare systems.Meta-analyses often estimate heterogeneity between studies, but these estimates can have large uncertainty, which must be considered when interpreting results. The extent of statistical heterogeneity influences decisions about combining studies, treatment applicability, and risk factors. However, the uncertainty in heterogeneity estimates is often overlooked, leading to potential misinterpretations. The I² statistic is commonly used to measure heterogeneity, but it has limitations, including low statistical power and large confidence intervals, especially with small numbers of studies. Confidence intervals should be considered when interpreting heterogeneity. A study of 1011 Cochrane meta-analyses and 50 genetic risk factor meta-analyses showed that 95% confidence intervals for I² often span a wide range of heterogeneity, making it difficult to draw conclusions about the true level of heterogeneity. This uncertainty is particularly significant when I² is estimated at 0%, as the upper confidence interval may still indicate high heterogeneity. The I² statistic is not precise, and 95% confidence intervals should always be provided. The clinical implications of this uncertainty are significant, and decisions about homogeneity or heterogeneity should be made cautiously. Performance indicators are increasingly used to monitor the quality of care in primary care settings. These indicators help assess the quality of services provided by primary care doctors and identify areas for improvement. Performance indicators are based on routinely collected data, electronic medical records, and sometimes surveys. In the UK, performance indicators have been introduced to link performance to general practitioners' pay, and public access to performance data is becoming more common. However, there are challenges in comparing performance indicators across different health systems and countries due to differences in data collection and coding standards. Standardization of clinical data and performance indicators is needed for meaningful international comparisons. While performance indicators have potential adverse consequences, such as overtreatment or neglect of certain areas, they are essential for quality improvement and performance management in healthcare systems.