Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
These biases highlight the importance of careful study design and execution in epidemiological research to minimize errors and provide reliable data. Each type of bias poses unique challenges, and their presence can weaken the credibility of study findings. Understanding the sources and mechanisms of bias equips researchers with the tools to design better studies and apply appropriate analytical adjustments. In this way, minimizing bias is not merely a technical task but a step toward ensuring that epidemiological research provides meaningful and actionable insights for public health.
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