Subplots are crucial for several reasons. Firstly, they help identify and understand risk factors that might not be evident in the broader study. For instance, a large-scale study on influenza might include subplots focusing on different age groups, geographic regions, or socioeconomic statuses. By doing so, researchers can pinpoint which subpopulations are at higher risk and why.
Secondly, subplots facilitate hypothesis testing. Researchers can formulate specific hypotheses about disease dynamics and test them within the confines of a subplot. This targeted approach can lead to more precise and actionable findings.
