Data interpretation is a critical aspect of epidemiology that can lead to conflicts. Different stakeholders may interpret the same data differently, leading to disputes over the severity of a health issue or the effectiveness of an intervention. Confounding factors, biases, and errors in data collection can further complicate interpretation. For example, the debate over the effectiveness of vaccines often involves conflicting interpretations of epidemiological data.
